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POS Tagging: Introduction
Heng Ji
hengji@cs.qc.cuny.eduFeb 2, 2008
Acknowledgement: some slides from Ralph Grishman, Nicolas Nicolov, J&M
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Some Administrative Stuff
Assignment 1 due on Feb 17 Textbook: required for assignments and final
exam
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Outline
Parts of speech (POS) Tagsets POS Tagging
Rule-based tagging Markup Format Open source Toolkits
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What is Part-of-Speech (POS)
Generally speaking, Word Classes (=POS) : Verb, Noun, Adjective, Adverb, Article, …
We can also include inflection: Verbs: Tense, number, … Nouns: Number, proper/common, … Adjectives: comparative, superlative, … …
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Parts of Speech
8 (ish) traditional parts of speech Noun, verb, adjective, preposition, adverb,
article, interjection, pronoun, conjunction, etc Called: parts-of-speech, lexical categories,
word classes, morphological classes, lexical tags...
Lots of debate within linguistics about the number, nature, and universality of these
We’ll completely ignore this debate.
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7 Traditional POS Categories
N noun chair, bandwidth, pacing
V verb study, debate, munch ADJ adj purple, tall, ridiculous ADV adverb unfortunately, slowly, P preposition of, by, to PRO pronoun I, me, mine DET determiner the, a, that, those
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POS Tagging
The process of assigning a part-of-speech or lexical class marker to each word in a collection.
WORD tag
the DETkoala Nput Vthe DETkeys Non Pthe DETtable N
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Penn TreeBank POS Tag Set
Penn Treebank: hand-annotated corpus of Wall Street Journal, 1M words
46 tags Some particularities:
to /TO not disambiguated Auxiliaries and verbs not distinguished
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Penn Treebank Tagset
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Why POS tagging is useful?
Speech synthesis: How to pronounce “lead”? INsult inSULT OBject obJECT OVERflow overFLOW DIScount disCOUNT CONtent conTENT
Stemming for information retrieval Can search for “aardvarks” get “aardvark”
Parsing and speech recognition and etc Possessive pronouns (my, your, her) followed by nouns Personal pronouns (I, you, he) likely to be followed by verbs Need to know if a word is an N or V before you can parse
Information extraction Finding names, relations, etc.
Machine Translation
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Equivalent Problem in Bioinformatics
Durbin et al. Biological Sequence Analysis, Cambridge University Press.
Several applications, e.g. proteins
From primary structure ATCPLELLLD
Infer secondary structure HHHBBBBBC..
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Why is POS Tagging Useful? First step of a vast number of practical tasks Speech synthesis
How to pronounce “lead”? INsult inSULT OBject obJECT OVERflow overFLOW DIScount disCOUNT CONtent conTENT
Parsing Need to know if a word is an N or V before you can parse
Information extraction Finding names, relations, etc.
Machine Translation
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Open and Closed Classes
Closed class: a small fixed membership Prepositions: of, in, by, … Auxiliaries: may, can, will had, been, … Pronouns: I, you, she, mine, his, them, … Usually function words (short common words which
play a role in grammar) Open class: new ones can be created all the time
English has 4: Nouns, Verbs, Adjectives, Adverbs Many languages have these 4, but not all!
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Open Class Words Nouns
Proper nouns (Boulder, Granby, Eli Manning) English capitalizes these.
Common nouns (the rest). Count nouns and mass nouns
Count: have plurals, get counted: goat/goats, one goat, two goats Mass: don’t get counted (snow, salt, communism) (*two snows)
Adverbs: tend to modify things Unfortunately, John walked home extremely slowly yesterday Directional/locative adverbs (here,home, downhill) Degree adverbs (extremely, very, somewhat) Manner adverbs (slowly, slinkily, delicately)
Verbs In English, have morphological affixes (eat/eats/eaten)
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Closed Class Words
Examples:
prepositions: on, under, over, … particles: up, down, on, off, … determiners: a, an, the, … pronouns: she, who, I, .. conjunctions: and, but, or, … auxiliary verbs: can, may should, … numerals: one, two, three, third, …
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Prepositions from CELEX
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English Particles
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Conjunctions
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POS TaggingChoosing a Tagset
There are so many parts of speech, potential distinctions we can draw
To do POS tagging, we need to choose a standard set of tags to work with
Could pick very coarse tagsets N, V, Adj, Adv.
More commonly used set is finer grained, the “Penn TreeBank tagset”, 45 tags PRP$, WRB, WP$, VBG
Even more fine-grained tagsets exist
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Using the Penn Tagset
The/DT grand/JJ jury/NN commmented/VBD on/IN a/DT number/NN of/IN other/JJ topics/NNS ./.
Prepositions and subordinating conjunctions marked IN (“although/IN I/PRP..”)
Except the preposition/complementizer “to” is just marked “TO”.
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POS Tagging
Words often have more than one POS: back The back door = JJ On my back = NN Win the voters back = RB Promised to back the bill = VB
The POS tagging problem is to determine the POS tag for a particular instance of a word.
These examples from Dekang Lin
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How Hard is POS Tagging? Measuring Ambiguity
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Current Performance
How many tags are correct? About 97% currently But baseline is already 90% Baseline algorithm:
Tag every word with its most frequent tag Tag unknown words as nouns
How well do people do?
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Quick Test: Agreement?
the students went to class plays well with others fruit flies like a banana
DT: the, this, thatNN: nounVB: verbP: prepostionADV: adverb
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Quick Test
the students went to class
DT NN VB P NN plays well with others
VB ADV P NN
NN NN P DT fruit flies like a banana
NN NN VB DT NN
NN VB P DT NN
NN NN P DT NN
NN VB VB DT NN
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How to do it? History
1960 1970 1980 1990 2000
Brown Corpus Created (EN-US)1 Million Words
Brown Corpus Tagged
HMM Tagging (CLAWS)93%-95%
Greene and RubinRule Based - 70%
LOB Corpus Created (EN-UK)1 Million Words
DeRose/ChurchEfficient HMMSparse Data
95%+
British National Corpus
(tagged by CLAWS)
POS Tagging separated from
other NLP
Transformation Based Tagging
(Eric Brill)Rule Based – 95%+
Tree-Based Statistics (Helmut Shmid)
Rule Based – 96%+
Neural Network 96%+
Trigram Tagger(Kempe)
96%+
Combined Methods98%+
Penn Treebank Corpus
(WSJ, 4.5M)
LOB Corpus Tagged
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Two Methods for POS Tagging
1. Rule-based tagging (ENGTWOL)
2. Stochastic1. Probabilistic sequence models
HMM (Hidden Markov Model) tagging MEMMs (Maximum Entropy Markov Models)
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Rule-Based Tagging
Start with a dictionary Assign all possible tags to words from the
dictionary Write rules by hand to selectively remove
tags Leaving the correct tag for each word.
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Rule-based taggers
Early POS taggers all hand-coded Most of these (Harris, 1962; Greene and Rubin,
1971) and the best of the recent ones, ENGTWOL (Voutilainen, 1995) based on a two-stage architecture Stage 1: look up word in lexicon to give list of potential
POSs Stage 2: Apply rules which certify or disallow tag
sequences Rules originally handwritten; more recently Machine
Learning methods can be used
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Start With a Dictionary• she: PRP• promised: VBN,VBD• to TO• back: VB, JJ, RB, NN• the: DT• bill: NN, VB
• Etc… for the ~100,000 words of English with more than 1 tag
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Assign Every Possible Tag
NN
RB
VBN JJ VB
PRP VBD TO VB DT NN
She promised to back the bill
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Write Rules to Eliminate Tags
Eliminate VBN if VBD is an option when VBN|VBD follows “<start> PRP”
NN
RB
JJ VB
PRP VBD TO VB DT NN
She promised to back the bill
VBN
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Stage 1 of ENGTWOL Tagging
First Stage: Run words through FST morphological analyzer to get all parts of speech.
Example: Pavlov had shown that salivation …
Pavlov PAVLOV N NOM SG PROPERhad HAVE V PAST VFIN SVO
HAVE PCP2 SVOshown SHOW PCP2 SVOO SVO SVthat ADV
PRON DEM SGDET CENTRAL DEM SGCS
salivation N NOM SG
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Stage 2 of ENGTWOL Tagging
Second Stage: Apply NEGATIVE constraints. Example: Adverbial “that” rule
Eliminates all readings of “that” except the one in “It isn’t that odd”
Given input: “that”If(+1 A/ADV/QUANT) ;if next word is adj/adv/quantifier
(+2 SENT-LIM) ;following which is E-O-S
(NOT -1 SVOC/A) ; and the previous word is not a
; verb like “consider” which
; allows adjective complements
; in “I consider that odd”
Then eliminate non-ADV tagsElse eliminate ADV
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Inline Mark-up
POS Tagginghttp://nlp.cs.qc.cuny.edu/wsj_pos.zip
Input FormatPierre Vinken, 61/CD years/NNS old , will join
the board as a nonexecutive director Nov. 29.
Output FormatPierre/NNP Vinken/NNP ,/, 61/CD years/NNS
old/JJ ,/, will/MD join/VB the/DT board/NN as/IN a/DT nonexecutive/JJ director/NN Nov./NNP 29/CD ./.
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POS Tagging Tools
NYU Prof. Ralph Grishman’s HMM POS tagger (in Java)http://nlp.cs.qc.cuny.edu/jet.zip
http://nlp.cs.qc.cuny.edu/jet_src.zip
http://www.cs.nyu.edu/cs/faculty/grishman/jet/license.html
Demo How it works:
Learned HMM: data/pos_hmm.txt
Source code: src/jet/HMM/HMMTagger.java
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POS Tagging Tools
Stanford tagger (Loglinear tagger ) http://nlp.stanford.edu/software/tagger.shtml
Brill tagger http://www.tech.plym.ac.uk/soc/staff/guidbugm/software/RULE_BASED
_TAGGER_V.1.14.tar.Z tagger LEXICON test BIGRAMS LEXICALRULEFULE
CONTEXTUALRULEFILE YamCha (SVM) http://chasen.org/~taku/software/yamcha/ MXPOST (Maximum Entropy) ftp://ftp.cis.upenn.edu/pub/adwait/jmx/ More complete list at: http://www-nlp.stanford.edu/links/statnlp.html#Taggers
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NLP Toolkits
Uniform CL Annotation Platform UIMA (IBM NLP platform): http://incubator.apache.org/uima/svn.html Mallet (UMASS): http://mallet.cs.umass.edu/index.php/Main_Page MinorThird (CMU): http://minorthird.sourceforge.net/ NLTK: http://nltk.sourceforge.net/
Natural langauge toolkit, with data sets Demo
Information Extraction Jet (NYU IE toolkit)
http://www.cs.nyu.edu/cs/faculty/grishman/jet/license.html Gate: http://gate.ac.uk/download/index.html
University of Sheffield IE toolkit Information Retrieval
INDRI: http://www.lemurproject.org/indri/ Information Retrieval toolkit
Machine Translation Compara: http://adamastor.linguateca.pt/COMPARA/Welcome.html ISI decoder: http://www.isi.edu/licensed-sw/rewrite-decoder/ MOSES: http://www.statmt.org/moses/
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Looking Ahead: Next Class
Machine Learning for POS Tagging: Hidden Markov Model
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