Upload
others
View
19
Download
1
Embed Size (px)
Citation preview
Syntactic TheoryHead-driven Phrase Structure Grammar (HPSG)
Yi Zhang
Department of Computational LinguisticsSaarland University
December 15th, 2009
Zhang (Saarland University) Syntactic Theory 15.12.2009 1 / 25
History of HPSG and its influences
HPSG1: Pollard and Sag (1987)Formalism (typed feature structures), subcategorization, LP rules,hierarchical lexiconHPSG2: Pollard and Sag (1994) Chapter 1-8The structure of signs, control theory, binding theoryHPSG3: Pollard and Sag (1994) Chapter 9 “Reflections andRevisions”Valence features SUBJ, COMPS, SPR
HPSG4, HPSG5, . . .Unbounded dependency constructions, linking theory, semanticrepresentation, argument realization, . . .
Zhang (Saarland University) Syntactic Theory 15.12.2009 2 / 25
History of HPSG and its influences (cont.)
The development of HPSG is influenced by contemoporary theories:Syntax
Generalized Phrase Structure Grammar (Gazdar, Klein, Pullum &Sag, 1985)Categorial Grammar (McGee Wood, 1993)Lexical-Functional Grammar (Kaplan & Bresnan, 1982)Construction Grammar (Goldberg, 1995)Government-Binding Theory (Haegeman, 1994)
SemanticsSituation Semantics (Barwise & Perry, 1983)Discourse Representation Theory (Kamp & Reyle, 1993)x
Zhang (Saarland University) Syntactic Theory 15.12.2009 3 / 25
HPSG vs. “Classical” Phrase Structure Grammar
SimilaritiesBoth are monostratal: every analysis is represented by a singlestructureGrammar rules have local scope: mother phrase and itsimmediate daughters
Zhang (Saarland University) Syntactic Theory 15.12.2009 4 / 25
HPSG vs. “Classical” Phrase Structure Grammar
DifferencesHPSG uses complex categories while classical PSG usessimple/atomic onesHPSG specifies Immediate Dominance (ID) and LinearPrecedence (LP) separately
ID specifies the mother and daughters in a local tree withoutspecifying the order of the daughtersLP determines the relative order of the daughters in a local treewithout making reference to the motherFurther universal principles are specified in HPSG to constrain theset of local trees admitted by the ID schemata
HPSG analyses include semantic representations in addition tosyntactic representations
Zhang (Saarland University) Syntactic Theory 15.12.2009 5 / 25
HPSG vs. Transformational Grammar
SimilaritiesBoth try to account for a similar range of data (e.g. in thedevelopment of the Binding Theory)Both are theories of generative grammar
Zhang (Saarland University) Syntactic Theory 15.12.2009 6 / 25
HPSG vs. Transformational Grammar
DifferencesHPSG is non-derivational, TG is derivational
TG analyses start with a base generated tree, which is then subjectto a variety of transformation (e.g., movement, deletion, reanalysis)that produce the desired surface structureHPSG analyses generate only the surface structure, rule ordering isirrelevant
HPSG constraints are local, TG allows non-local statementsHPSG uses more complex categories than TGHPSG is more committed to precise formalization than TGHPSG is better suited to computational implementation than TG
Zhang (Saarland University) Syntactic Theory 15.12.2009 7 / 25
Key Properties of HPSG and their consequences
HPSG is monostratal, declarative, non-derivationalNo transformations, no rule ordering. Analyses are surfaceoriented, with a desire to avoid abstract structure such as tracesand functional categoriesHPSG is constraint-basedA structure is well-formed if and only if it satisfies all relevantconstraints. Constraints are not violable (as in Optimality Theory,for example)HPSG is a lexicalist theoryStrong lexicalism; Word-internal structures and phrase structureare handled separatelyHPSG is a unification-based linguistic framework where alllinguistic objects are represented as “typed feature structures”
Zhang (Saarland University) Syntactic Theory 15.12.2009 8 / 25
Psycholinguistic Evidence
Human language processing is incremental:Partial interpretations can be generated for partial utterancesHPSG constraints can apply to partial structures as well ascomplete treesHLP is integrative:Linguistic interpretations depend on a large amount ofnon-linguistic information (e.g. world knowledge)The signs in HPSG can incorporate both linguistic andnon-linguistic information using the same formal representation
Zhang (Saarland University) Syntactic Theory 15.12.2009 9 / 25
Psycholinguistic Evidence
HLP is order-independent:There is no fixed sequence in which pieces of information areconsulted and incorporated into a linguistic interpretationHPSG is a declarative and non-derivational modelHLP is reversible:Utterances can be understood and generatedHPSG is process-neutral, and can be applied for either productionor comprehension
Zhang (Saarland University) Syntactic Theory 15.12.2009 10 / 25
Signs in HPSG
Sign is the basic sort/type in HPSG used to describe lexical items (oftype word) and phrases (of type phrase). All signs carry the followingtwo features:
PHON encodes the phonological representation of the signSYNSEM syntax and semantics
sign
[PHON list(phon-string)SYNSEM synsem
]
Zhang (Saarland University) Syntactic Theory 15.12.2009 11 / 25
Structure of the Signs in HPSG
synsem introduces the features LOCAL and NON-LOCAL
local introduces CATEGORY (CAT) CONTENT (CONT) and CONTEXT
(CONX)non-local will be discussed in connection with unboundeddependenciescategory includes the syntactic category and the grammaticalargument of the word/phrase
Zhang (Saarland University) Syntactic Theory 15.12.2009 12 / 25
An Ontology of Linguistic Objects
sign
"PHON list(phon-string)SYNSEM synsem
#
word phrase
hDTRS constituent-struc
i
synsem
"LOCAL localNON-LOCAL non-local
#local
264CATEGORY categoryCONTENT contentCONTEXT context
375category
264HEAD headVAL . . .. . .
375
Zhang (Saarland University) Syntactic Theory 15.12.2009 13 / 25
Structure of the Signs in HPSG (cont.)
Example
word
266666666666666666666666666666664
PHOND
sheE
SYNSEM
synsem
26666666666666666666666666664
LOCAL
local
2666666666666666666666666664
CATEGORY
cat
2666664HEAD
noun
hCASE nom
iVALENCE
val
264SUBJ 〈〉COMPS 〈〉SPR 〈〉
375
3777775
CONTENT
ppro
266664INDEX 1
ref
264PER 3rdNUM singGEND fem
375RESTR {}
377775
CONTEXT
context
264BACKGR
8<:psoa
"RELN female
INST 1
#9=;375
3777777777777777777777777775
37777777777777777777777777775
377777777777777777777777777777775
Zhang (Saarland University) Syntactic Theory 15.12.2009 14 / 25
Syntactic Category & Valence
The value of CATEGORY encode information aboutThe sign’s syntactic category (“part-of-speech”)
Given via the feature[
HEAD head], where head is the supertype
for noun, verb, adjective, preposition, determiner, marker; each ofthese types selects a particular set of head features
The sign’s subcategorzation frame/valence, i.e. its potential tocombine with other signs to form larger phrases
Three list-valued featuresSYNSEM|LOC|CAT|VALENCE
valence
SUBJECT list(synsem)SPECIFIER list(synsem)COMPLEMENTS list(synsem)
If any of these lists are non-empty (“unsaturated” ), the sign has thepotential to combine with another sign
Zhang (Saarland University) Syntactic Theory 15.12.2009 15 / 25
Head Information
head
functional
hSPEC synsem
isubstantive
"PRD boolean. . .
#
marker determiner
adjective
verb
264VFORM vformAUX booleanINV boolean
375noun
hCASE case
iprep
hPFORM pform
i. . .
Zhang (Saarland University) Syntactic Theory 15.12.2009 16 / 25
Features of head Types
vform
finite infinitive base gerund present-part. past-part. passive-part.
case
nominative accusative
pform
of to . . .
Zhang (Saarland University) Syntactic Theory 15.12.2009 17 / 25
Valence Features
The VALENCE lists take as values the list of synsems instead ofsignsThis means that word does not have access to the DTRS list ofitems on its valence listsMore discussion on different valence lists will follow when weintroduce the valence principle and ID schemata
Zhang (Saarland University) Syntactic Theory 15.12.2009 18 / 25
Semantic Representation
Semantic interpretation of the sign is given as the value to CONTENT
nominal-object: an individual/entity (or a set of them), associatedwith a referring index, bearing agreement featuresparameterized-state-of-affairs: a partial situate; an event relationalong with role names for identifying the participants of the eventquantifier: some, all, every, a, the, . . .
Note: many of these have been reformulated by “MinimalRecursion Semantics” which allows underspecification ofquantifier scopes, though a in-depth discussion of MRS is beyondthe scope of this class
Zhang (Saarland University) Syntactic Theory 15.12.2009 19 / 25
Semantic Representation
Semantic interpretation of the sign is given as the value to CONTENT
nominal-object: an individual/entity (or a set of them), associatedwith a referring index, bearing agreement featuresparameterized-state-of-affairs: a partial situate; an event relationalong with role names for identifying the participants of the eventquantifier: some, all, every, a, the, . . .
Note: many of these have been reformulated by “MinimalRecursion Semantics” which allows underspecification ofquantifier scopes, though a in-depth discussion of MRS is beyondthe scope of this class
Zhang (Saarland University) Syntactic Theory 15.12.2009 19 / 25
Semantic Representation
content
. . . psoanom-obj
"INDEX indexRESTR set(psoa)
#
laugh’
hLAUGHER ref
igive’
264GIVER refGIVEN refGIFT ref
375drink’
"DRINKER refDRUNKEN ref
#think’
"THINKER refTHOUGHT psoa
#
Zhang (Saarland University) Syntactic Theory 15.12.2009 20 / 25
Indices
index
264PERSON personNUMBER numberGENDER gender
375
referential there it
person
first second third
number
singular plural
gender
masculine feminine neuter
Zhang (Saarland University) Syntactic Theory 15.12.2009 21 / 25
Auxiliary Data Structures
>
boolean
+ −
list
elistnelist
"FIRST >REST list
#. . .
Zhang (Saarland University) Syntactic Theory 15.12.2009 22 / 25
Some List Abbreviations
Empty list (elist) is abbreviated as⟨⟩
nelist
[FIRST 1
REST 2
]is abbreviated as
⟨1 | 2
⟩⟨
. . . 1 |〈〉⟩
is equivalent to⟨
. . . 1⟩
nelist
FIRST 1
REST
nelist
[FIRST 2
REST 3
] is equivalent to⟨
1 , 2 | 3⟩
⟨>
⟩and
⟨1⟩
describe all lists of length one
Zhang (Saarland University) Syntactic Theory 15.12.2009 23 / 25
Abbreviations of Common AVMs
The following abbreviations are used to describe synsem objects:
NP1
synsem
2666666664LOCAL
local
266666664CAT
cat
266664HEAD noun
VAL
val
264SUBJ 〈〉COMPS 〈〉SPR 〈〉
375377775
CONT | INDEX 1
377777775
3777777775
S: 1
synsem
2666666664LOCAL
local
266666664CAT
cat
266664HEAD verb
VAL
val
264SUBJ 〈〉COMPS 〈〉SPR 〈〉
375377775
CONT 1
377777775
3777777775
VP: 1
synsem
266666666664LOCAL
local
26666666664CAT
cat
2666664HEAD verb
VAL
val
2664SUBJD
SYNSEME
COMPS 〈〉SPR 〈〉
37753777775
CONT 1
37777777775
377777777775
Zhang (Saarland University) Syntactic Theory 15.12.2009 24 / 25
References I
Pollard, C. J. and Sag, I. A. (1994).Head-Driven Phrase Structure Grammar.University of Chicago Press, Chicago, USA.
Zhang (Saarland University) Syntactic Theory 15.12.2009 25 / 25