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E-TEXT in E-FLFour flavours
1 Przemek Kaszubski
2 Joanna Jendryczka-Wierszycka
3 Michał Remiszewski
4 Włodzimierz Sobkowiak
flexibility fonts formats attributes correctibility accuracy up-to-dateness searchability local and global portability PDA Kindle smartphone etc manipulability types media channels annotability tagging parsing semantic web immediacy speed of transmission and processing (hyper-)linkability nonlinearity sharability openness low cost popularity among digital natives
(See The Machine is Using Us by Michael Wesch for a good video treatment of these issues)
The advantages of e-text
PK IFAConc - web-concordancing with EAP writing students
JJW e-text annotation - why bother
MR Towards competence mapping in language teaching
learning
WS e-text in Second Life reification of text
Presentation plan
Przemysław Kaszubski
IFAConc ndash web-concordancing with EAP writing students
Developersndash Paweł Nowakndash Dominique Stranz
Over 200 Student Participantsndash 12 MA and BA seminars 2005-6ndash 16 1MA and 2MA seminars 2007-8ndash 18 1BA Writing 2007-8ndash 32 1MA Academic Writing 2008-9ndash 140 3MA Acad Discourse Part-Time Lecture 2008-9
Acknowledgements
a form of e-text processing for a linguistic purpose descriptive or pedagogical
ndash paper concordance lt computerised concordancingndash data-driven learning (DDL) operationalisation of gap-noticing (also form-
focused instruction awareness-raising)
lsquoshuntingrsquo (Halliday)ndash vertical paradigmatic reading ndash KWiC ndash horizontal syntagmatic reading ndash KWiC + context
pedagogic concordancing for EAPESP learningndash repetitions patterns (light theory lsquoextended units of meaningrsquo ndash Sinclair
lsquolexical primingsrsquo ndash Hoey)ndash dispersion within corpusndash variation across corpora
Concordancing
Corpora Search(click on picture to go to IFAConc log in for best effect)
DDL under-practised and under-researched ndash few dedicated student-friendly tools Some needs
ndash facilitate training and current practice (time factors what to search for and how inductive analysis)
ndash facilitate (but not replace) noticing and deeper-processingndash manage resultsndash facilitate teacher control and teacher-student interactionndash integrate with syllabus etc (also lsquonon-e-textrsquo)
IFAConc and EAP writing ndash some assumptions
ndash trace relevant academic primings (interesting patterns are many)ndash students (meta)linguistically conscious = co-research possiblendash enable more complex search patterns and subtle observationsndash encourage autonomy and individualisation (personal lsquoprimingsrsquo)
DDL issues and IFAConc
e-text sample collection of e-text samples (= corpus cline of spec corpora) selective structural markup (XML) linguistic annotation (POS tagging) conc searchability (syntax language + options) conc manipulability sampling re-sorting corpus switching automatic conc summary stats table collocate counting unique URL search address ndash hyperlinking note-taking (annotation) ndash personal andor T-S collaborative search logging (personal and global History database ndash
browsable searchable hyper-linkable towards dynamic conc-illustrated EAP textbook (Resources)
E-text integration in IFAConc
History(click on picture to go to IFAConc History log in when prompted)
Resources(click on picture to go to IFAConc Resources ndash regrsquod IFA users only)
hyperlink-assisted concordancingndash Corpora Search hyperlinksndash History search hyperlinks
also Corpora Search ID and History Search ID optionsndash integrated with other materials
eg feedback links resources for self-exploration T-S interactive annotation = less time-costly more meaningful concordancing
ndash more students conduct more searches that are more in-depth ndash teacher learns about studentsrsquo linguistic and cognitive abilitiesndash while the database of relevant lg observations continues to
grow (and to gradually feed lsquoSharedrsquo History and Resources)
Beyond bottom-up concordancing
IFAConc (092006 ndash 022009) ndash 206 participantsndash All searches gt 37000ndash Students All searches gt 17000ndash All annotated gt 2200ndash Studentsrsquo annotated ndash c 1000
PICLE Conc (042004 ndash 082005)ndash 125 IP numbers (15-20 active users)ndash All ()studentsrsquo searches lt 3700ndash Studentsrsquo annotated (non-interactive) ndash about 40
Concordancing with EAP students ndash basic stats
ndash ldquoI found this research valuable as I used a few examples from Concordance database in my MA dissertation I value the research as it provides me with proper examples of native uses Whenever I look for a word usage I Google it yet it never gives me 100 certainty that the internet source is a reliable one Conc on the other hand is a reliable tool which a student can trustrdquo (agooska H-37145)
ndash ldquoI regret I didnrsquot search these Conc pages before I wrote the majority of my dissertationhellipIt is really a vital source - very helpfulrdquo (Aleksandra Resources Textbook comment)
Some more practical applications will be shown at ELT training on 27th March
Testimonials
Joanna Jendryczka-Wierszycka
e-text annotation - why bother
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
flexibility fonts formats attributes correctibility accuracy up-to-dateness searchability local and global portability PDA Kindle smartphone etc manipulability types media channels annotability tagging parsing semantic web immediacy speed of transmission and processing (hyper-)linkability nonlinearity sharability openness low cost popularity among digital natives
(See The Machine is Using Us by Michael Wesch for a good video treatment of these issues)
The advantages of e-text
PK IFAConc - web-concordancing with EAP writing students
JJW e-text annotation - why bother
MR Towards competence mapping in language teaching
learning
WS e-text in Second Life reification of text
Presentation plan
Przemysław Kaszubski
IFAConc ndash web-concordancing with EAP writing students
Developersndash Paweł Nowakndash Dominique Stranz
Over 200 Student Participantsndash 12 MA and BA seminars 2005-6ndash 16 1MA and 2MA seminars 2007-8ndash 18 1BA Writing 2007-8ndash 32 1MA Academic Writing 2008-9ndash 140 3MA Acad Discourse Part-Time Lecture 2008-9
Acknowledgements
a form of e-text processing for a linguistic purpose descriptive or pedagogical
ndash paper concordance lt computerised concordancingndash data-driven learning (DDL) operationalisation of gap-noticing (also form-
focused instruction awareness-raising)
lsquoshuntingrsquo (Halliday)ndash vertical paradigmatic reading ndash KWiC ndash horizontal syntagmatic reading ndash KWiC + context
pedagogic concordancing for EAPESP learningndash repetitions patterns (light theory lsquoextended units of meaningrsquo ndash Sinclair
lsquolexical primingsrsquo ndash Hoey)ndash dispersion within corpusndash variation across corpora
Concordancing
Corpora Search(click on picture to go to IFAConc log in for best effect)
DDL under-practised and under-researched ndash few dedicated student-friendly tools Some needs
ndash facilitate training and current practice (time factors what to search for and how inductive analysis)
ndash facilitate (but not replace) noticing and deeper-processingndash manage resultsndash facilitate teacher control and teacher-student interactionndash integrate with syllabus etc (also lsquonon-e-textrsquo)
IFAConc and EAP writing ndash some assumptions
ndash trace relevant academic primings (interesting patterns are many)ndash students (meta)linguistically conscious = co-research possiblendash enable more complex search patterns and subtle observationsndash encourage autonomy and individualisation (personal lsquoprimingsrsquo)
DDL issues and IFAConc
e-text sample collection of e-text samples (= corpus cline of spec corpora) selective structural markup (XML) linguistic annotation (POS tagging) conc searchability (syntax language + options) conc manipulability sampling re-sorting corpus switching automatic conc summary stats table collocate counting unique URL search address ndash hyperlinking note-taking (annotation) ndash personal andor T-S collaborative search logging (personal and global History database ndash
browsable searchable hyper-linkable towards dynamic conc-illustrated EAP textbook (Resources)
E-text integration in IFAConc
History(click on picture to go to IFAConc History log in when prompted)
Resources(click on picture to go to IFAConc Resources ndash regrsquod IFA users only)
hyperlink-assisted concordancingndash Corpora Search hyperlinksndash History search hyperlinks
also Corpora Search ID and History Search ID optionsndash integrated with other materials
eg feedback links resources for self-exploration T-S interactive annotation = less time-costly more meaningful concordancing
ndash more students conduct more searches that are more in-depth ndash teacher learns about studentsrsquo linguistic and cognitive abilitiesndash while the database of relevant lg observations continues to
grow (and to gradually feed lsquoSharedrsquo History and Resources)
Beyond bottom-up concordancing
IFAConc (092006 ndash 022009) ndash 206 participantsndash All searches gt 37000ndash Students All searches gt 17000ndash All annotated gt 2200ndash Studentsrsquo annotated ndash c 1000
PICLE Conc (042004 ndash 082005)ndash 125 IP numbers (15-20 active users)ndash All ()studentsrsquo searches lt 3700ndash Studentsrsquo annotated (non-interactive) ndash about 40
Concordancing with EAP students ndash basic stats
ndash ldquoI found this research valuable as I used a few examples from Concordance database in my MA dissertation I value the research as it provides me with proper examples of native uses Whenever I look for a word usage I Google it yet it never gives me 100 certainty that the internet source is a reliable one Conc on the other hand is a reliable tool which a student can trustrdquo (agooska H-37145)
ndash ldquoI regret I didnrsquot search these Conc pages before I wrote the majority of my dissertationhellipIt is really a vital source - very helpfulrdquo (Aleksandra Resources Textbook comment)
Some more practical applications will be shown at ELT training on 27th March
Testimonials
Joanna Jendryczka-Wierszycka
e-text annotation - why bother
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
PK IFAConc - web-concordancing with EAP writing students
JJW e-text annotation - why bother
MR Towards competence mapping in language teaching
learning
WS e-text in Second Life reification of text
Presentation plan
Przemysław Kaszubski
IFAConc ndash web-concordancing with EAP writing students
Developersndash Paweł Nowakndash Dominique Stranz
Over 200 Student Participantsndash 12 MA and BA seminars 2005-6ndash 16 1MA and 2MA seminars 2007-8ndash 18 1BA Writing 2007-8ndash 32 1MA Academic Writing 2008-9ndash 140 3MA Acad Discourse Part-Time Lecture 2008-9
Acknowledgements
a form of e-text processing for a linguistic purpose descriptive or pedagogical
ndash paper concordance lt computerised concordancingndash data-driven learning (DDL) operationalisation of gap-noticing (also form-
focused instruction awareness-raising)
lsquoshuntingrsquo (Halliday)ndash vertical paradigmatic reading ndash KWiC ndash horizontal syntagmatic reading ndash KWiC + context
pedagogic concordancing for EAPESP learningndash repetitions patterns (light theory lsquoextended units of meaningrsquo ndash Sinclair
lsquolexical primingsrsquo ndash Hoey)ndash dispersion within corpusndash variation across corpora
Concordancing
Corpora Search(click on picture to go to IFAConc log in for best effect)
DDL under-practised and under-researched ndash few dedicated student-friendly tools Some needs
ndash facilitate training and current practice (time factors what to search for and how inductive analysis)
ndash facilitate (but not replace) noticing and deeper-processingndash manage resultsndash facilitate teacher control and teacher-student interactionndash integrate with syllabus etc (also lsquonon-e-textrsquo)
IFAConc and EAP writing ndash some assumptions
ndash trace relevant academic primings (interesting patterns are many)ndash students (meta)linguistically conscious = co-research possiblendash enable more complex search patterns and subtle observationsndash encourage autonomy and individualisation (personal lsquoprimingsrsquo)
DDL issues and IFAConc
e-text sample collection of e-text samples (= corpus cline of spec corpora) selective structural markup (XML) linguistic annotation (POS tagging) conc searchability (syntax language + options) conc manipulability sampling re-sorting corpus switching automatic conc summary stats table collocate counting unique URL search address ndash hyperlinking note-taking (annotation) ndash personal andor T-S collaborative search logging (personal and global History database ndash
browsable searchable hyper-linkable towards dynamic conc-illustrated EAP textbook (Resources)
E-text integration in IFAConc
History(click on picture to go to IFAConc History log in when prompted)
Resources(click on picture to go to IFAConc Resources ndash regrsquod IFA users only)
hyperlink-assisted concordancingndash Corpora Search hyperlinksndash History search hyperlinks
also Corpora Search ID and History Search ID optionsndash integrated with other materials
eg feedback links resources for self-exploration T-S interactive annotation = less time-costly more meaningful concordancing
ndash more students conduct more searches that are more in-depth ndash teacher learns about studentsrsquo linguistic and cognitive abilitiesndash while the database of relevant lg observations continues to
grow (and to gradually feed lsquoSharedrsquo History and Resources)
Beyond bottom-up concordancing
IFAConc (092006 ndash 022009) ndash 206 participantsndash All searches gt 37000ndash Students All searches gt 17000ndash All annotated gt 2200ndash Studentsrsquo annotated ndash c 1000
PICLE Conc (042004 ndash 082005)ndash 125 IP numbers (15-20 active users)ndash All ()studentsrsquo searches lt 3700ndash Studentsrsquo annotated (non-interactive) ndash about 40
Concordancing with EAP students ndash basic stats
ndash ldquoI found this research valuable as I used a few examples from Concordance database in my MA dissertation I value the research as it provides me with proper examples of native uses Whenever I look for a word usage I Google it yet it never gives me 100 certainty that the internet source is a reliable one Conc on the other hand is a reliable tool which a student can trustrdquo (agooska H-37145)
ndash ldquoI regret I didnrsquot search these Conc pages before I wrote the majority of my dissertationhellipIt is really a vital source - very helpfulrdquo (Aleksandra Resources Textbook comment)
Some more practical applications will be shown at ELT training on 27th March
Testimonials
Joanna Jendryczka-Wierszycka
e-text annotation - why bother
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Przemysław Kaszubski
IFAConc ndash web-concordancing with EAP writing students
Developersndash Paweł Nowakndash Dominique Stranz
Over 200 Student Participantsndash 12 MA and BA seminars 2005-6ndash 16 1MA and 2MA seminars 2007-8ndash 18 1BA Writing 2007-8ndash 32 1MA Academic Writing 2008-9ndash 140 3MA Acad Discourse Part-Time Lecture 2008-9
Acknowledgements
a form of e-text processing for a linguistic purpose descriptive or pedagogical
ndash paper concordance lt computerised concordancingndash data-driven learning (DDL) operationalisation of gap-noticing (also form-
focused instruction awareness-raising)
lsquoshuntingrsquo (Halliday)ndash vertical paradigmatic reading ndash KWiC ndash horizontal syntagmatic reading ndash KWiC + context
pedagogic concordancing for EAPESP learningndash repetitions patterns (light theory lsquoextended units of meaningrsquo ndash Sinclair
lsquolexical primingsrsquo ndash Hoey)ndash dispersion within corpusndash variation across corpora
Concordancing
Corpora Search(click on picture to go to IFAConc log in for best effect)
DDL under-practised and under-researched ndash few dedicated student-friendly tools Some needs
ndash facilitate training and current practice (time factors what to search for and how inductive analysis)
ndash facilitate (but not replace) noticing and deeper-processingndash manage resultsndash facilitate teacher control and teacher-student interactionndash integrate with syllabus etc (also lsquonon-e-textrsquo)
IFAConc and EAP writing ndash some assumptions
ndash trace relevant academic primings (interesting patterns are many)ndash students (meta)linguistically conscious = co-research possiblendash enable more complex search patterns and subtle observationsndash encourage autonomy and individualisation (personal lsquoprimingsrsquo)
DDL issues and IFAConc
e-text sample collection of e-text samples (= corpus cline of spec corpora) selective structural markup (XML) linguistic annotation (POS tagging) conc searchability (syntax language + options) conc manipulability sampling re-sorting corpus switching automatic conc summary stats table collocate counting unique URL search address ndash hyperlinking note-taking (annotation) ndash personal andor T-S collaborative search logging (personal and global History database ndash
browsable searchable hyper-linkable towards dynamic conc-illustrated EAP textbook (Resources)
E-text integration in IFAConc
History(click on picture to go to IFAConc History log in when prompted)
Resources(click on picture to go to IFAConc Resources ndash regrsquod IFA users only)
hyperlink-assisted concordancingndash Corpora Search hyperlinksndash History search hyperlinks
also Corpora Search ID and History Search ID optionsndash integrated with other materials
eg feedback links resources for self-exploration T-S interactive annotation = less time-costly more meaningful concordancing
ndash more students conduct more searches that are more in-depth ndash teacher learns about studentsrsquo linguistic and cognitive abilitiesndash while the database of relevant lg observations continues to
grow (and to gradually feed lsquoSharedrsquo History and Resources)
Beyond bottom-up concordancing
IFAConc (092006 ndash 022009) ndash 206 participantsndash All searches gt 37000ndash Students All searches gt 17000ndash All annotated gt 2200ndash Studentsrsquo annotated ndash c 1000
PICLE Conc (042004 ndash 082005)ndash 125 IP numbers (15-20 active users)ndash All ()studentsrsquo searches lt 3700ndash Studentsrsquo annotated (non-interactive) ndash about 40
Concordancing with EAP students ndash basic stats
ndash ldquoI found this research valuable as I used a few examples from Concordance database in my MA dissertation I value the research as it provides me with proper examples of native uses Whenever I look for a word usage I Google it yet it never gives me 100 certainty that the internet source is a reliable one Conc on the other hand is a reliable tool which a student can trustrdquo (agooska H-37145)
ndash ldquoI regret I didnrsquot search these Conc pages before I wrote the majority of my dissertationhellipIt is really a vital source - very helpfulrdquo (Aleksandra Resources Textbook comment)
Some more practical applications will be shown at ELT training on 27th March
Testimonials
Joanna Jendryczka-Wierszycka
e-text annotation - why bother
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Developersndash Paweł Nowakndash Dominique Stranz
Over 200 Student Participantsndash 12 MA and BA seminars 2005-6ndash 16 1MA and 2MA seminars 2007-8ndash 18 1BA Writing 2007-8ndash 32 1MA Academic Writing 2008-9ndash 140 3MA Acad Discourse Part-Time Lecture 2008-9
Acknowledgements
a form of e-text processing for a linguistic purpose descriptive or pedagogical
ndash paper concordance lt computerised concordancingndash data-driven learning (DDL) operationalisation of gap-noticing (also form-
focused instruction awareness-raising)
lsquoshuntingrsquo (Halliday)ndash vertical paradigmatic reading ndash KWiC ndash horizontal syntagmatic reading ndash KWiC + context
pedagogic concordancing for EAPESP learningndash repetitions patterns (light theory lsquoextended units of meaningrsquo ndash Sinclair
lsquolexical primingsrsquo ndash Hoey)ndash dispersion within corpusndash variation across corpora
Concordancing
Corpora Search(click on picture to go to IFAConc log in for best effect)
DDL under-practised and under-researched ndash few dedicated student-friendly tools Some needs
ndash facilitate training and current practice (time factors what to search for and how inductive analysis)
ndash facilitate (but not replace) noticing and deeper-processingndash manage resultsndash facilitate teacher control and teacher-student interactionndash integrate with syllabus etc (also lsquonon-e-textrsquo)
IFAConc and EAP writing ndash some assumptions
ndash trace relevant academic primings (interesting patterns are many)ndash students (meta)linguistically conscious = co-research possiblendash enable more complex search patterns and subtle observationsndash encourage autonomy and individualisation (personal lsquoprimingsrsquo)
DDL issues and IFAConc
e-text sample collection of e-text samples (= corpus cline of spec corpora) selective structural markup (XML) linguistic annotation (POS tagging) conc searchability (syntax language + options) conc manipulability sampling re-sorting corpus switching automatic conc summary stats table collocate counting unique URL search address ndash hyperlinking note-taking (annotation) ndash personal andor T-S collaborative search logging (personal and global History database ndash
browsable searchable hyper-linkable towards dynamic conc-illustrated EAP textbook (Resources)
E-text integration in IFAConc
History(click on picture to go to IFAConc History log in when prompted)
Resources(click on picture to go to IFAConc Resources ndash regrsquod IFA users only)
hyperlink-assisted concordancingndash Corpora Search hyperlinksndash History search hyperlinks
also Corpora Search ID and History Search ID optionsndash integrated with other materials
eg feedback links resources for self-exploration T-S interactive annotation = less time-costly more meaningful concordancing
ndash more students conduct more searches that are more in-depth ndash teacher learns about studentsrsquo linguistic and cognitive abilitiesndash while the database of relevant lg observations continues to
grow (and to gradually feed lsquoSharedrsquo History and Resources)
Beyond bottom-up concordancing
IFAConc (092006 ndash 022009) ndash 206 participantsndash All searches gt 37000ndash Students All searches gt 17000ndash All annotated gt 2200ndash Studentsrsquo annotated ndash c 1000
PICLE Conc (042004 ndash 082005)ndash 125 IP numbers (15-20 active users)ndash All ()studentsrsquo searches lt 3700ndash Studentsrsquo annotated (non-interactive) ndash about 40
Concordancing with EAP students ndash basic stats
ndash ldquoI found this research valuable as I used a few examples from Concordance database in my MA dissertation I value the research as it provides me with proper examples of native uses Whenever I look for a word usage I Google it yet it never gives me 100 certainty that the internet source is a reliable one Conc on the other hand is a reliable tool which a student can trustrdquo (agooska H-37145)
ndash ldquoI regret I didnrsquot search these Conc pages before I wrote the majority of my dissertationhellipIt is really a vital source - very helpfulrdquo (Aleksandra Resources Textbook comment)
Some more practical applications will be shown at ELT training on 27th March
Testimonials
Joanna Jendryczka-Wierszycka
e-text annotation - why bother
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
a form of e-text processing for a linguistic purpose descriptive or pedagogical
ndash paper concordance lt computerised concordancingndash data-driven learning (DDL) operationalisation of gap-noticing (also form-
focused instruction awareness-raising)
lsquoshuntingrsquo (Halliday)ndash vertical paradigmatic reading ndash KWiC ndash horizontal syntagmatic reading ndash KWiC + context
pedagogic concordancing for EAPESP learningndash repetitions patterns (light theory lsquoextended units of meaningrsquo ndash Sinclair
lsquolexical primingsrsquo ndash Hoey)ndash dispersion within corpusndash variation across corpora
Concordancing
Corpora Search(click on picture to go to IFAConc log in for best effect)
DDL under-practised and under-researched ndash few dedicated student-friendly tools Some needs
ndash facilitate training and current practice (time factors what to search for and how inductive analysis)
ndash facilitate (but not replace) noticing and deeper-processingndash manage resultsndash facilitate teacher control and teacher-student interactionndash integrate with syllabus etc (also lsquonon-e-textrsquo)
IFAConc and EAP writing ndash some assumptions
ndash trace relevant academic primings (interesting patterns are many)ndash students (meta)linguistically conscious = co-research possiblendash enable more complex search patterns and subtle observationsndash encourage autonomy and individualisation (personal lsquoprimingsrsquo)
DDL issues and IFAConc
e-text sample collection of e-text samples (= corpus cline of spec corpora) selective structural markup (XML) linguistic annotation (POS tagging) conc searchability (syntax language + options) conc manipulability sampling re-sorting corpus switching automatic conc summary stats table collocate counting unique URL search address ndash hyperlinking note-taking (annotation) ndash personal andor T-S collaborative search logging (personal and global History database ndash
browsable searchable hyper-linkable towards dynamic conc-illustrated EAP textbook (Resources)
E-text integration in IFAConc
History(click on picture to go to IFAConc History log in when prompted)
Resources(click on picture to go to IFAConc Resources ndash regrsquod IFA users only)
hyperlink-assisted concordancingndash Corpora Search hyperlinksndash History search hyperlinks
also Corpora Search ID and History Search ID optionsndash integrated with other materials
eg feedback links resources for self-exploration T-S interactive annotation = less time-costly more meaningful concordancing
ndash more students conduct more searches that are more in-depth ndash teacher learns about studentsrsquo linguistic and cognitive abilitiesndash while the database of relevant lg observations continues to
grow (and to gradually feed lsquoSharedrsquo History and Resources)
Beyond bottom-up concordancing
IFAConc (092006 ndash 022009) ndash 206 participantsndash All searches gt 37000ndash Students All searches gt 17000ndash All annotated gt 2200ndash Studentsrsquo annotated ndash c 1000
PICLE Conc (042004 ndash 082005)ndash 125 IP numbers (15-20 active users)ndash All ()studentsrsquo searches lt 3700ndash Studentsrsquo annotated (non-interactive) ndash about 40
Concordancing with EAP students ndash basic stats
ndash ldquoI found this research valuable as I used a few examples from Concordance database in my MA dissertation I value the research as it provides me with proper examples of native uses Whenever I look for a word usage I Google it yet it never gives me 100 certainty that the internet source is a reliable one Conc on the other hand is a reliable tool which a student can trustrdquo (agooska H-37145)
ndash ldquoI regret I didnrsquot search these Conc pages before I wrote the majority of my dissertationhellipIt is really a vital source - very helpfulrdquo (Aleksandra Resources Textbook comment)
Some more practical applications will be shown at ELT training on 27th March
Testimonials
Joanna Jendryczka-Wierszycka
e-text annotation - why bother
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Corpora Search(click on picture to go to IFAConc log in for best effect)
DDL under-practised and under-researched ndash few dedicated student-friendly tools Some needs
ndash facilitate training and current practice (time factors what to search for and how inductive analysis)
ndash facilitate (but not replace) noticing and deeper-processingndash manage resultsndash facilitate teacher control and teacher-student interactionndash integrate with syllabus etc (also lsquonon-e-textrsquo)
IFAConc and EAP writing ndash some assumptions
ndash trace relevant academic primings (interesting patterns are many)ndash students (meta)linguistically conscious = co-research possiblendash enable more complex search patterns and subtle observationsndash encourage autonomy and individualisation (personal lsquoprimingsrsquo)
DDL issues and IFAConc
e-text sample collection of e-text samples (= corpus cline of spec corpora) selective structural markup (XML) linguistic annotation (POS tagging) conc searchability (syntax language + options) conc manipulability sampling re-sorting corpus switching automatic conc summary stats table collocate counting unique URL search address ndash hyperlinking note-taking (annotation) ndash personal andor T-S collaborative search logging (personal and global History database ndash
browsable searchable hyper-linkable towards dynamic conc-illustrated EAP textbook (Resources)
E-text integration in IFAConc
History(click on picture to go to IFAConc History log in when prompted)
Resources(click on picture to go to IFAConc Resources ndash regrsquod IFA users only)
hyperlink-assisted concordancingndash Corpora Search hyperlinksndash History search hyperlinks
also Corpora Search ID and History Search ID optionsndash integrated with other materials
eg feedback links resources for self-exploration T-S interactive annotation = less time-costly more meaningful concordancing
ndash more students conduct more searches that are more in-depth ndash teacher learns about studentsrsquo linguistic and cognitive abilitiesndash while the database of relevant lg observations continues to
grow (and to gradually feed lsquoSharedrsquo History and Resources)
Beyond bottom-up concordancing
IFAConc (092006 ndash 022009) ndash 206 participantsndash All searches gt 37000ndash Students All searches gt 17000ndash All annotated gt 2200ndash Studentsrsquo annotated ndash c 1000
PICLE Conc (042004 ndash 082005)ndash 125 IP numbers (15-20 active users)ndash All ()studentsrsquo searches lt 3700ndash Studentsrsquo annotated (non-interactive) ndash about 40
Concordancing with EAP students ndash basic stats
ndash ldquoI found this research valuable as I used a few examples from Concordance database in my MA dissertation I value the research as it provides me with proper examples of native uses Whenever I look for a word usage I Google it yet it never gives me 100 certainty that the internet source is a reliable one Conc on the other hand is a reliable tool which a student can trustrdquo (agooska H-37145)
ndash ldquoI regret I didnrsquot search these Conc pages before I wrote the majority of my dissertationhellipIt is really a vital source - very helpfulrdquo (Aleksandra Resources Textbook comment)
Some more practical applications will be shown at ELT training on 27th March
Testimonials
Joanna Jendryczka-Wierszycka
e-text annotation - why bother
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
DDL under-practised and under-researched ndash few dedicated student-friendly tools Some needs
ndash facilitate training and current practice (time factors what to search for and how inductive analysis)
ndash facilitate (but not replace) noticing and deeper-processingndash manage resultsndash facilitate teacher control and teacher-student interactionndash integrate with syllabus etc (also lsquonon-e-textrsquo)
IFAConc and EAP writing ndash some assumptions
ndash trace relevant academic primings (interesting patterns are many)ndash students (meta)linguistically conscious = co-research possiblendash enable more complex search patterns and subtle observationsndash encourage autonomy and individualisation (personal lsquoprimingsrsquo)
DDL issues and IFAConc
e-text sample collection of e-text samples (= corpus cline of spec corpora) selective structural markup (XML) linguistic annotation (POS tagging) conc searchability (syntax language + options) conc manipulability sampling re-sorting corpus switching automatic conc summary stats table collocate counting unique URL search address ndash hyperlinking note-taking (annotation) ndash personal andor T-S collaborative search logging (personal and global History database ndash
browsable searchable hyper-linkable towards dynamic conc-illustrated EAP textbook (Resources)
E-text integration in IFAConc
History(click on picture to go to IFAConc History log in when prompted)
Resources(click on picture to go to IFAConc Resources ndash regrsquod IFA users only)
hyperlink-assisted concordancingndash Corpora Search hyperlinksndash History search hyperlinks
also Corpora Search ID and History Search ID optionsndash integrated with other materials
eg feedback links resources for self-exploration T-S interactive annotation = less time-costly more meaningful concordancing
ndash more students conduct more searches that are more in-depth ndash teacher learns about studentsrsquo linguistic and cognitive abilitiesndash while the database of relevant lg observations continues to
grow (and to gradually feed lsquoSharedrsquo History and Resources)
Beyond bottom-up concordancing
IFAConc (092006 ndash 022009) ndash 206 participantsndash All searches gt 37000ndash Students All searches gt 17000ndash All annotated gt 2200ndash Studentsrsquo annotated ndash c 1000
PICLE Conc (042004 ndash 082005)ndash 125 IP numbers (15-20 active users)ndash All ()studentsrsquo searches lt 3700ndash Studentsrsquo annotated (non-interactive) ndash about 40
Concordancing with EAP students ndash basic stats
ndash ldquoI found this research valuable as I used a few examples from Concordance database in my MA dissertation I value the research as it provides me with proper examples of native uses Whenever I look for a word usage I Google it yet it never gives me 100 certainty that the internet source is a reliable one Conc on the other hand is a reliable tool which a student can trustrdquo (agooska H-37145)
ndash ldquoI regret I didnrsquot search these Conc pages before I wrote the majority of my dissertationhellipIt is really a vital source - very helpfulrdquo (Aleksandra Resources Textbook comment)
Some more practical applications will be shown at ELT training on 27th March
Testimonials
Joanna Jendryczka-Wierszycka
e-text annotation - why bother
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
e-text sample collection of e-text samples (= corpus cline of spec corpora) selective structural markup (XML) linguistic annotation (POS tagging) conc searchability (syntax language + options) conc manipulability sampling re-sorting corpus switching automatic conc summary stats table collocate counting unique URL search address ndash hyperlinking note-taking (annotation) ndash personal andor T-S collaborative search logging (personal and global History database ndash
browsable searchable hyper-linkable towards dynamic conc-illustrated EAP textbook (Resources)
E-text integration in IFAConc
History(click on picture to go to IFAConc History log in when prompted)
Resources(click on picture to go to IFAConc Resources ndash regrsquod IFA users only)
hyperlink-assisted concordancingndash Corpora Search hyperlinksndash History search hyperlinks
also Corpora Search ID and History Search ID optionsndash integrated with other materials
eg feedback links resources for self-exploration T-S interactive annotation = less time-costly more meaningful concordancing
ndash more students conduct more searches that are more in-depth ndash teacher learns about studentsrsquo linguistic and cognitive abilitiesndash while the database of relevant lg observations continues to
grow (and to gradually feed lsquoSharedrsquo History and Resources)
Beyond bottom-up concordancing
IFAConc (092006 ndash 022009) ndash 206 participantsndash All searches gt 37000ndash Students All searches gt 17000ndash All annotated gt 2200ndash Studentsrsquo annotated ndash c 1000
PICLE Conc (042004 ndash 082005)ndash 125 IP numbers (15-20 active users)ndash All ()studentsrsquo searches lt 3700ndash Studentsrsquo annotated (non-interactive) ndash about 40
Concordancing with EAP students ndash basic stats
ndash ldquoI found this research valuable as I used a few examples from Concordance database in my MA dissertation I value the research as it provides me with proper examples of native uses Whenever I look for a word usage I Google it yet it never gives me 100 certainty that the internet source is a reliable one Conc on the other hand is a reliable tool which a student can trustrdquo (agooska H-37145)
ndash ldquoI regret I didnrsquot search these Conc pages before I wrote the majority of my dissertationhellipIt is really a vital source - very helpfulrdquo (Aleksandra Resources Textbook comment)
Some more practical applications will be shown at ELT training on 27th March
Testimonials
Joanna Jendryczka-Wierszycka
e-text annotation - why bother
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
History(click on picture to go to IFAConc History log in when prompted)
Resources(click on picture to go to IFAConc Resources ndash regrsquod IFA users only)
hyperlink-assisted concordancingndash Corpora Search hyperlinksndash History search hyperlinks
also Corpora Search ID and History Search ID optionsndash integrated with other materials
eg feedback links resources for self-exploration T-S interactive annotation = less time-costly more meaningful concordancing
ndash more students conduct more searches that are more in-depth ndash teacher learns about studentsrsquo linguistic and cognitive abilitiesndash while the database of relevant lg observations continues to
grow (and to gradually feed lsquoSharedrsquo History and Resources)
Beyond bottom-up concordancing
IFAConc (092006 ndash 022009) ndash 206 participantsndash All searches gt 37000ndash Students All searches gt 17000ndash All annotated gt 2200ndash Studentsrsquo annotated ndash c 1000
PICLE Conc (042004 ndash 082005)ndash 125 IP numbers (15-20 active users)ndash All ()studentsrsquo searches lt 3700ndash Studentsrsquo annotated (non-interactive) ndash about 40
Concordancing with EAP students ndash basic stats
ndash ldquoI found this research valuable as I used a few examples from Concordance database in my MA dissertation I value the research as it provides me with proper examples of native uses Whenever I look for a word usage I Google it yet it never gives me 100 certainty that the internet source is a reliable one Conc on the other hand is a reliable tool which a student can trustrdquo (agooska H-37145)
ndash ldquoI regret I didnrsquot search these Conc pages before I wrote the majority of my dissertationhellipIt is really a vital source - very helpfulrdquo (Aleksandra Resources Textbook comment)
Some more practical applications will be shown at ELT training on 27th March
Testimonials
Joanna Jendryczka-Wierszycka
e-text annotation - why bother
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Resources(click on picture to go to IFAConc Resources ndash regrsquod IFA users only)
hyperlink-assisted concordancingndash Corpora Search hyperlinksndash History search hyperlinks
also Corpora Search ID and History Search ID optionsndash integrated with other materials
eg feedback links resources for self-exploration T-S interactive annotation = less time-costly more meaningful concordancing
ndash more students conduct more searches that are more in-depth ndash teacher learns about studentsrsquo linguistic and cognitive abilitiesndash while the database of relevant lg observations continues to
grow (and to gradually feed lsquoSharedrsquo History and Resources)
Beyond bottom-up concordancing
IFAConc (092006 ndash 022009) ndash 206 participantsndash All searches gt 37000ndash Students All searches gt 17000ndash All annotated gt 2200ndash Studentsrsquo annotated ndash c 1000
PICLE Conc (042004 ndash 082005)ndash 125 IP numbers (15-20 active users)ndash All ()studentsrsquo searches lt 3700ndash Studentsrsquo annotated (non-interactive) ndash about 40
Concordancing with EAP students ndash basic stats
ndash ldquoI found this research valuable as I used a few examples from Concordance database in my MA dissertation I value the research as it provides me with proper examples of native uses Whenever I look for a word usage I Google it yet it never gives me 100 certainty that the internet source is a reliable one Conc on the other hand is a reliable tool which a student can trustrdquo (agooska H-37145)
ndash ldquoI regret I didnrsquot search these Conc pages before I wrote the majority of my dissertationhellipIt is really a vital source - very helpfulrdquo (Aleksandra Resources Textbook comment)
Some more practical applications will be shown at ELT training on 27th March
Testimonials
Joanna Jendryczka-Wierszycka
e-text annotation - why bother
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
hyperlink-assisted concordancingndash Corpora Search hyperlinksndash History search hyperlinks
also Corpora Search ID and History Search ID optionsndash integrated with other materials
eg feedback links resources for self-exploration T-S interactive annotation = less time-costly more meaningful concordancing
ndash more students conduct more searches that are more in-depth ndash teacher learns about studentsrsquo linguistic and cognitive abilitiesndash while the database of relevant lg observations continues to
grow (and to gradually feed lsquoSharedrsquo History and Resources)
Beyond bottom-up concordancing
IFAConc (092006 ndash 022009) ndash 206 participantsndash All searches gt 37000ndash Students All searches gt 17000ndash All annotated gt 2200ndash Studentsrsquo annotated ndash c 1000
PICLE Conc (042004 ndash 082005)ndash 125 IP numbers (15-20 active users)ndash All ()studentsrsquo searches lt 3700ndash Studentsrsquo annotated (non-interactive) ndash about 40
Concordancing with EAP students ndash basic stats
ndash ldquoI found this research valuable as I used a few examples from Concordance database in my MA dissertation I value the research as it provides me with proper examples of native uses Whenever I look for a word usage I Google it yet it never gives me 100 certainty that the internet source is a reliable one Conc on the other hand is a reliable tool which a student can trustrdquo (agooska H-37145)
ndash ldquoI regret I didnrsquot search these Conc pages before I wrote the majority of my dissertationhellipIt is really a vital source - very helpfulrdquo (Aleksandra Resources Textbook comment)
Some more practical applications will be shown at ELT training on 27th March
Testimonials
Joanna Jendryczka-Wierszycka
e-text annotation - why bother
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
IFAConc (092006 ndash 022009) ndash 206 participantsndash All searches gt 37000ndash Students All searches gt 17000ndash All annotated gt 2200ndash Studentsrsquo annotated ndash c 1000
PICLE Conc (042004 ndash 082005)ndash 125 IP numbers (15-20 active users)ndash All ()studentsrsquo searches lt 3700ndash Studentsrsquo annotated (non-interactive) ndash about 40
Concordancing with EAP students ndash basic stats
ndash ldquoI found this research valuable as I used a few examples from Concordance database in my MA dissertation I value the research as it provides me with proper examples of native uses Whenever I look for a word usage I Google it yet it never gives me 100 certainty that the internet source is a reliable one Conc on the other hand is a reliable tool which a student can trustrdquo (agooska H-37145)
ndash ldquoI regret I didnrsquot search these Conc pages before I wrote the majority of my dissertationhellipIt is really a vital source - very helpfulrdquo (Aleksandra Resources Textbook comment)
Some more practical applications will be shown at ELT training on 27th March
Testimonials
Joanna Jendryczka-Wierszycka
e-text annotation - why bother
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
ndash ldquoI found this research valuable as I used a few examples from Concordance database in my MA dissertation I value the research as it provides me with proper examples of native uses Whenever I look for a word usage I Google it yet it never gives me 100 certainty that the internet source is a reliable one Conc on the other hand is a reliable tool which a student can trustrdquo (agooska H-37145)
ndash ldquoI regret I didnrsquot search these Conc pages before I wrote the majority of my dissertationhellipIt is really a vital source - very helpfulrdquo (Aleksandra Resources Textbook comment)
Some more practical applications will be shown at ELT training on 27th March
Testimonials
Joanna Jendryczka-Wierszycka
e-text annotation - why bother
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Joanna Jendryczka-Wierszycka
e-text annotation - why bother
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
annotation (tagging)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Facebook Picasa Gmail Etc
Linguistic (e-text) annotation
annotation (tagging)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
definition
different levels of annotation explanations examples and utility
limitations of annotation
answer to ldquoWhy botherrdquo
e-Text annotation - contents
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)
It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)
e-Text annotation defined
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Part-of-Speech
Parsing
Semantic
Discourse pragmatic
Stylistic
Prosodic
Lemmatization
Markup
e-Text annotation exemplified
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
adding information about word classes
er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV
er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1
e-Text annotation - POS
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
by far most frequent annotation
useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis
POS-tagging ctd
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
syntactic analysis into such units as phrases and clauses (sentence structure)
[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _
[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG
big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N
Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ
arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ
home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1
park_NNL1 ]N]P]N]P]V] _ S]
e-Text annotation - parsing
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
adding information about the semantic category of words eg ldquobarkrdquo
for translation and lexicography
PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4
e-Text annotation - semantics
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
adding information about anaphoric links eg for MT
S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision
e-Text annotation - discourse anaphora
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog
ltIP MOD=interactivegtokltIPgt
ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt
ltIP SA=requestgt May I open the window pleaseltIPgt
e-Text annotation - pragmatics
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)
SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)
ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir
e-Text annotation - stylistics
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
segmental pronunciation
prosodic boundaries prominent syllables and abnormal sound lengthening
Both highly valuable in accent studies
ik heb hem | n^e^gen maal ontvangen denk ik
speaker A jan | en ook piet waren hier al eerder twee jaar geleden
speaker B ja| dat weet ik || maar wanneer
e-Text annotation - prosody
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
lemmatization = adding the identity of the lemma (base form) of each word form in a text
markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)
also markup for speakerwriter identification useful in sociolinguistics
e-Text annotation - lemmatization amp markup
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
accuracy
annotation= always interpretation Its never theory free (MWUs -ing)
ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent
the importance to keep ldquopurerdquo text separately (Sinclair)
which one how where when applied and by whom
Limitations of annotation
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)
fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics
ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004
References
Why bother
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Michał Remiszewski
Towards competence mapping in language teachinglearning
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Technology-driven
Practice-driven
Reasons for e-learning
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Structured syllabus
No access to the structure of competence
Problem
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Synchronic view
Dynamic view
Solution competence mapping
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
CLIP AMBER ONE
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
It will allow the creation and administration of interactive language tasks for learners
It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words
It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher
It will help identify problem areas and dynamics in learnersrsquo linguistic competence
AMBER ONE
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Włodzimierz Sobkowiak
e-text in Second Life reification of text
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
public text-chat
Instant Messaging (IM)
notecards
whiteboards
object info fields
avatar profile info fields
inventory contents
menu system
Types of ordinary e-text in SL
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Linguistic symbols from phonemesletters to whole texts can be
reified into rezzed (created) three-dimensional objects thus
creating innovative manipulative affordances impossible in First
Life and appealing especially to kinaesthetic learners For
example phonetic dominoes words reified as moveable and
audio-enhanced blocks which attract or repel each other
according to e-FL-relevant phonetic criteria such as segmental
makeup syllable number stress pattern etc
Unique e-text affordances in SL
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Phonetic dominoes (view from above)
Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-
clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Phonetic dominoesclose-up view of pork chops
Youll find my dominoes in my Virtlantis classroom
in Second Life
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)
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