Computational Grammars Azadeh Maghsoodi History Before 1800 1900

Computational Grammars Azadeh Maghsoodi

History Before 1800 -1900 First 20 s World War II Last 1950 s Nowadays

Before 1800 l l l Traditional Grammar Correct Speech of a specific language Not scientific Rejected Useful issues: POS

1800 -1900 l l l Indian-European languages Language vs. Other languages Language vs. its history

Early 20 s l l Enough Philology! Language in a specific time

20 s l l l America & Western Europe Intellectual Pattern Understanding Processes in human being

World War II l l l Math. Logic as a study tool Computer invention caused new App Abstract Mind model ends Behaviorism

Late 1950 l l Chomsky is coming! Formal Language Theory “Syntactic Structures” Language Categories – – Type 0: Natural (Irregular) Type 1: Context sensitive Type 2: Context free Type 3: Regular

Late 1950 (continue) l Chomsky followers professes: – Generative grammar: Accurate and definite enough for testing l – – Generative Grammars Goal: Unaware knowledge of users Biologic and inborn basis for linguistic abilities l l Universal Grammar Shared structures

Nowadays l Motives – – l Discover human mind structure Language process technology Applications – – – Word processors MT Word predictors Text predictors UFIs / DB Queries Information retrieval

Syntactic Model Grammars Parse Algorithms

Computational Grammars l Generative Grammars – – – Caused by Natural Language Theory Introduced by Chomsky Accurate and definite structures Transformational grammar (TG) Constraint-Based Lexicalist grammar (CBLG)

TG l l Less computational efficiency Theoretical basis Complex rules Simple lexicons

TG (continue) l l Chomsky hierarchy & First TG Standard Theory (1965) Extended Standard Theory Government & Binding Theory (1981 -1988)

Standard Theory l Sentence – – l Deep structure Surface structure Generative TG – Basic part l l – Produce deep structure CFG Transformational part l Transformational Rules

Transformational Rules l l l Convert deep structure to surface structure Transformational Rule ~ Transformation Example: (same deep structures) – – – (i) The boys place the book on the table. (ii) The boy has placed the book on the table. (iii) Did the boy place the book on the table?

Transformational Rules (example) l A deep structure: S NP N VP D the Aux boy V will NP place The book

Transformational Rules (example) S NP S VP Aux V NP VP NP V l NP To produce yes/no question: – – Using a Move Transformation S[NP VP [AUX V NP]] S[AUX NP VP[V NP]]

Government and Binding Theory (GB) l l l Universal grammar theory Learning a language = confirming a small set of parameters + learning lexicons Move α: deep structure to surface structure ‘Move α’ moves anything to anywhere Some constraints correct ‘Move α’

GB (continue) Lexicons Deep Struct Move-α Surface Struct Stylistic & Phonological Rules Phonological Form LF Move-α Logical Form

GB (continue) l Minimalist Program (MP) – – Choose the best candidate instead of direct production Under study

CBLG l l l Based on TGs Increase computational efficiency of grammars Simple rules Complex lexicons Psychological Computational

CBLG (continue) l Constraint-Based architecture – l Constraint satisfaction more important than transformational derivation Strict lexicalism – – Lexicons: syntactic atoms of a language Independent Internal structure from syntactic constraints

CBLG (continue) l l Surface structures are produced directly Most computational grammars are CBLG

Computational Grammars l l l l Unification grammar (UG) Categorical grammar (CG) Dependency grammar (DG) Link grammar Lexical/Functional grammar (LFG) Tree Adjoining grammar (TAG) Generalized Phrase Structure grammar (GPSG) Head Driven Phrase Structure grammar (HPSG)

Unification Grammar (UG) l l Lots of CBLs are UG Augmented CFG – – l CFG can’t recognize long distance dependencies A generalized form of CFG + A set of features Augmented Transition Network (ATN) Definite Clause Grammar (DCG) Unification Grammars

UG (continue) l Unification Grammars – – Feature structures are extended No need to CFGs Grammar ~ A set of constraints between feature structures Key concept: Subsumption relation

UG (continue) CAT verb ROOT cry CAT verb VFORM present (Unificator) CAT verb ROOT cry VFORM present

UG (example) S NP VP l Unification grammar: X 0 X 1 X 2 CAT 0 = 5 CAT 1 = NP CAT 2 VP AGR 0 = AGR 1 = AGR 2 VFORM 0 = VORM 2

UG (continue) l l l More grammar information are stored in lexicons Less grammar rules Using DAGs

ATN Grammar l l Transitive network ~ Expanded Finite-State machine ATN Grammar ~ A set of transitive networks Features Constraints

Categorical Grammar (CG) l l l Lots of bases are omitted No difference between lexicons and nonelexicons Part Of Speech is replaced by some complex category NP/S : NP is on the right NPS : NP is on the left

CG (example) Peter : NP Likes : (NPS)/NP Peanuts : NP Passionately : (NPS)(NPS) Peter likes peanuts passionately.

CG (example) S NP NPS Peter (NPS)(NPS)/NP NP Likes peanuts passionately

Dependency Grammar (DG) l l American linguists Based on TGs Dependencies between words Dependency tree V N boys play Adv well

Link Grammar l l Planarity phenomenon Legal sequence of words: – – – l l CFG Lexical grammars – l l l Satisfy local necessities (satisfaction) No crossed conjunctions (planarity) One connected graph (connectivity) Grammar is distributed between words Probability models Voice recognition Hand-written recognition

Link Grammar (example) linking requirements:

Link Grammar (example) linking requirements are satisfied

Link grammar (example) Not part of a language

Lexical-Functional Grammar (LFG) l l l Unification grammar Not TG ATN research and its deficiencies introduced LFG Group structures 4 structures

Tree Adjoining Grammar (TAG) l l Between CFG and CSG Grammar rules are a set of initial trees Initial trees are anchored trees Two main operations: – – l Substitution Adjoin High accuracy

TAG (example) S NP VP VP + VP V S ADV NP NP VP VP V ADV NP

TAG (continue) l l High accuracy Apps in NLP – – – MT Information retrieval …

Generalized Phrase Structure grammar (GPSG) l l Only CFLs CFG Rules – – Immediate Dominance (ID) Linear Precedence (LP)

Head Driven Phrase Structure grammar (HPSG) l l l Lexical grammar Based on unification Increase computational potency of GPSG Simple CFG Complex lexicons

Applications

Parse Algorithms l l Top-Down parsing Bottom-Up parsing (*)

Parse Algorithms l l Top-Down parsing Chart parser – l Recursive Transition Network (RTN) – l ATN grammar LR parser – l Dynamic Programming Shift-Reduce algorithms Cocke-Younger-Kasami parser (CYK) – – Dynamic Programming CNF grammar

Efficient Algorithms l l Chart parser CYK parser

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