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Computational Grammars

Computational Grammars

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Computational Grammars

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  1. Computational Grammars Azadeh Maghsoodi

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

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

  4. 1800-1900 • Indian-European languages • Language vs. Other languages • Language vs. its history

  5. Early 20s • Enough Philology! • Language in a specific time

  6. 20s • America & Western Europe • Intellectual Pattern • Understanding Processes in human being

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

  8. Late 1950 • 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

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

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

  11. Syntactic Model Grammars Parse Algorithms

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

  13. TG • Less computational efficiency • Theoretical basis • Complex rules • Simple lexicons

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

  15. Standard Theory • Sentence • Deep structure • Surface structure • Generative TG • Basic part • Produce deep structure • CFG • Transformational part • Transformational Rules

  16. Transformational Rules • 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?

  17. Transformational Rules (example) • A deep structure:

  18. Transformational Rules (example) Move Transformation • To produce yes/no question: • Using a Move Transformation • S[NP VP [AUX V NP]] S[AUX NP VP[V NP]]

  19. Government and Binding Theory (GB) • 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’

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

  21. GB (continue) • Minimalist Program (MP) • Choose the best candidate instead of direct production • Under study

  22. CBLG • Based on TGs • Increase computational efficiency of grammars • Simple rules • Complex lexicons • Psychological • Computational

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

  24. CBLG (continue) • Surface structures are produced directly • Most computational grammars are CBLG

  25. Computational Grammars • 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)

  26. Unification Grammar (UG) • Lots of CBLs are UG • Augmented CFG • 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

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

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

  29. UG (example) S NP VP • Unification grammar: X0 X1 X2 CAT 0 = 5 CAT 1 = NP CAT 2 VP AGR 0 = AGR 1 = AGR 2 VFORM 0 = VORM 2

  30. UG (continue) • More grammar information are stored in lexicons • Less grammar rules • Using DAGs

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

  32. Categorical Grammar (CG) • Lots of bases are omitted • No difference between lexicons and none-lexicons • Part Of Speech is replaced by some complex category • NP/S : NP is on the right • NP\S : NP is on the left

  33. CG (example) Peter : NP Likes : (NP\S)/NP Peanuts : NP Passionately : (NP\S)\(NP\S) Peter likes peanuts passionately.

  34. CG (example)

  35. Dependency Grammar (DG) • American linguists • Based on TGs • Dependencies between words • Dependency tree

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

  37. Link Grammar (example) linking requirements:

  38. Link Grammar (example) linking requirements are satisfied

  39. Link grammar (example) Not part of a language

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

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

  42. TAG (example) S VP S NP VP + VP ADV  NP VP V NP VP ADV V NP

  43. TAG (continue) • High accuracy • Apps in NLP • MT • Information retrieval • …

  44. Generalized Phrase Structure grammar (GPSG) • Only CFLs • CFG Rules • Immediate Dominance (ID) • Linear Precedence (LP)

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

  46. Applications

  47. Parse Algorithms • Top-Down parsing • Bottom-Up parsing (*)

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

  49. Efficient Algorithms • Chart parser • CYK parser

  50. Questions???