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Introduction to Computational Linguistics

Introduction to Computational Linguistics. Martha Palmer April 19, 2006. Natural Language Processing. Machine Translation Predicate argument structures Syntactic parses Producing semantic representations Ambiguities in sentence interpretation. Machine Translation.

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Introduction to Computational Linguistics

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  1. Introduction to Computational Linguistics Martha Palmer April 19, 2006 NLP

  2. Natural Language Processing • Machine Translation • Predicate argument structures • Syntactic parses • Producing semantic representations • Ambiguities in sentence interpretation NLP

  3. Machine Translation • One of the first applications for computers • bilingual dictionary > word-word translation • Good translation requires understanding! • War and Peace, The Sound and The Fury? • What can we do? Sublanguages. • technical domains, static vocabulary • Meteo in Canada, Caterpillar Tractor Manuals, Botanical descriptions, Military Messages NLP

  4. Example translation NLP

  5. Translation Issues: Korean to English - Word order - Dropped arguments - Lexical ambiguities - Structure vs morphology NLP

  6. Common Thread • Predicate-argument structure • Basic constituents of the sentence and how they are related to each other • Constituents • John, Mary, the dog, pleasure, the store. • Relations • Loves, feeds, go, to, bring NLP

  7. Abstracting away from surface structure NLP

  8. Transfer lexicons NLP

  9. Machine Translation Lexical Choice- Word Sense Disambiguation • Iraq lost the battle. • Ilakuka centwey ciessta. • [Iraq ] [battle] [lost]. • John lost his computer. • John-i computer-lul ilepelyessta. • [John] [computer] [misplaced]. NLP

  10. Natural Language Processing • Syntax • Grammars, parsers, parse trees, dependency structures • Semantics • Subcategorization frames, semantic classes, ontologies, formal semantics • Pragmatics • Pronouns, reference resolution, discourse models NLP

  11. Syntactic Categories • Nouns, pronouns, Proper nouns • Verbs, intransitive verbs, transitive verbs, ditransitive verbs (subcategorization frames) • Modifiers, Adjectives, Adverbs • Prepositions • Conjunctions NLP

  12. Syntactic Parsing • The cat sat on the mat. Det Noun Verb Prep Det Noun • Time flies like an arrow. Noun Verb Prep Det Noun • Fruit flies like a banana. Noun Noun Verb Det Noun NLP

  13. Context Free Grammar • S -> NP VP • NP -> det (adj) N • NP -> Proper N • NP -> N • VP -> V, VP -> V PP • VP -> V NP • VP -> V NP PP, PP -> Prep NP • VP -> V NP NP NLP

  14. Parses The cat sat on the mat S NP VP Det PP N V the cat sat NP Prep N on Det mat the NLP

  15. Parses Time flies like an arrow. S NP VP N time V PP flies Prep NP like Det N arrow an NLP

  16. Parses Time flies like an arrow. S NP VP N time V NP N like flies N Det arrow an NLP

  17. Features • C for Case, Subjective/Objective • She visited her. • P for Person agreement, (1st, 2nd, 3rd) • I like him, You like him, He likes him, • N for Number agreement, Subject/Verb • He likes him, They like him. • G for Gender agreement, Subject/Verb • English, reflexive pronouns He washed himself. • Romance languages, det/noun • T for Tense, • auxiliaries, sentential complements, etc. • * will finished is bad NLP

  18. Probabilistic Context Free Grammars • Adding probabilities • Lexicalizing the probabilities NLP

  19. Simple Context Free Grammar in BNF S → NP VP NP → Pronoun | Noun | Det Adj Noun |NP PP PP → Prep NP V → Verb | Aux Verb VP → V | V NP | V NP NP | V NP PP | VP PP NLP

  20. Simple Probabilistic CFG S → NP VP NP → Pronoun [0.10] | Noun [0.20] | Det Adj Noun [0.50] |NP PP [0.20] PP → Prep NP [1.00] V → Verb [0.33] | Aux Verb [0.67] VP → V [0.10] | V NP [0.40] | V NP NP [0.10] | V NP PP [0.20] | VP PP [0.20] NLP

  21. Simple Probabilistic Lexicalized CFG S → NP VP NP → Pronoun [0.10] | Noun [0.20] | Det Adj Noun [0.50] |NP PP [0.20] PP → Prep NP [1.00] V → Verb [0.33] | Aux Verb [0.67] VP → V [0.87] {sleep, cry, laugh} | V NP [0.03] | V NP NP [0.00] | V NP PP [0.00] | VP PP [0.10] NLP

  22. Simple Probabilistic Lexicalized CFG VP → V [0.30] | V NP [0.60] {break,split,crack..} | V NP NP [0.00] | V NP PP [0.00] | VP PP [0.10] VP → V [0.10] what about | V NP [0.40] leave? | V NP NP [0.10] leave1, leave2? | V NP PP [0.20] | VP PP [0.20] NLP

  23. Language to Logic • John went to the book store. Johnstore1, go(John, store1) • John bought a book. buy(John,book1) • John gave the book to Mary. give(John,book1,Mary) • Mary put the book on the table. put(Mary,book1,table1) NLP

  24. SemanticsSame event - different sentences • John broke the window with a hammer. • John broke the window with the crack. • The hammer broke the window. • The window broke. NLP

  25. Same event - different syntactic frames • John broke the window with a hammer. • SUBJ VERB OBJ MODIFIER • John broke the window with the crack. • SUBJ VERB OBJ MODIFIER • The hammer broke the window. • SUBJ VERB OBJ • The window broke. • SUBJ VERB NLP

  26. Semantics -predicate arguments • break(AGENT, INSTRUMENT, PATIENT) • AGENT PATIENT INSTRUMENT • John broke the window with a hammer. • INSTRUMENT PATIENT • The hammer broke the window. • PATIENT • The window broke. • Fillmore 68 - The case for case NLP

  27. AGENT PATIENT INSTRUMENT • John broke the window with a hammer. • SUBJ OBJ MODIFIER • INSTRUMENT PATIENT • The hammer broke the window. • SUBJ OBJ • PATIENT • The window broke. • SUBJ NLP

  28. Canonical Representation • break (Agent: animate, • Instrument: tool, • Patient: physical-object) • Agent <=> subj • Instrument <=> subj, with-pp • Patient <=> obj, subj NLP

  29. Syntax/semantics interaction • Parsers will produce syntactically valid parses for semantically anomalous sentences • Lexical semantics can be used to rule them out NLP

  30. Headlines • Police Begin Campaign To Run Down Jaywalkers • Iraqi Head Seeks Arms • Teacher Strikes Idle Kids • Miners Refuse To Work After Death • Juvenile Court To Try Shooting Defendant NLP

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