Fall 2005 Lecture Notes #2

1. EECS 595 / LING 541 / SI 661&761 Natural Language Processing Fall 2005 Lecture Notes #2

2. Course logistics • Instructor: Prof. Dragomir Radev (radev@umich.edu) Ph.D., Computer Science, Columbia University Formerly at IBM TJ Watson Research Center • Times: Thursdays 2:40-5:25 PM, in 411, West Hall • Office hours: TBA, 3080 West Hall Connector Course home page: http://www.si.umich.edu/~radev/NLP-fall2005

3. Linguistic Fundamentals

4. Syntactic categories • Substitution test: { } black Persian tabbysmall Nathalie likes cats. • Open (lexical) and closed (functional) categories: No-fly-zone yadda yadda yadda the in

5. Morphology The dog chased the yellow bird. • Parts of speech: eight (or so) general types • Inflection (number, person, tense…) • Derivation (adjective-adverb, noun-verb) • Compounding (separate words or single word) • Part-of-speech tagging • Morphological analysis (prefix, root, suffix, ending)

6. Part of speech tags From Church (1991) - 79 tags NN /* singular noun */ IN /* preposition */ AT /* article */ NP /* proper noun */ JJ /* adjective */ , /* comma */ NNS /* plural noun */ CC /* conjunction */ RB /* adverb */ VB /* un-inflected verb */ VBN /* verb +en (taken, looked (passive,perfect)) */ VBD /* verb +ed (took, looked (past tense)) */ CS /* subordinating conjunction */

7. Jabberwocky (Lewis Carroll) `Twas brillig, and the slithy tovesDid gyre and gimble in the wabe:All mimsy were the borogoves,And the mome raths outgrabe."Beware the Jabberwock, my son!The jaws that bite, the claws that catch!Beware the Jubjub bird, and shunThe frumious Bandersnatch!"

8. Nouns • Nouns: dog, tree, computer, idea • Nouns vary in number (singular, plural), gender (masculine, feminine, neuter), case (nominative, genitive, accusative, dative) • Latin: filius (m), filia (f), filium (object)German: Mädchen • Clitics (‘s)

9. Pronouns • Pronouns: she, ourselves, mine • Pronouns vary in person, gender, number, case (in English: nominative, accusative, possessive, 2nd possessive, reflexive) Mary saw her in the mirror. Mary saw herself in the mirror. • Anaphors: herself, each other

10. Determiners and adjectives • Articles: the, a • Demonstratives: this, that • Adjectives: describe properties • Attributive and predicative adjectives • Agreement: in gender, number • Comparative and superlative (derivative and periphrastic) • Positive form

11. Verbs • Actions, activities, and states (throw, walk, have) • English: four verb forms • tenses: present, past, future • other inflection: number, person • gerunds and infinitive • aspect: progressive, perfective • voice: active, passive • participles, auxiliaries • irregular verbs • French and Finnish: many more inflections than English

12. Other parts of speech • Adverbs, prepositions, particles • phrasal verbs (the plane took off, take it off) • particles vs. prepositions (she ran up a bill/hill) • Coordinating conjunctions: and, or, but • Subordinating conjunctions: if, because, that, although • Interjections: Ouch!

13. Phrase structure • Constraints on word order • Constituents: NP, PP, VP, AP • Phrase structure grammars S NP VP PN V N N Det chased Spot a bird

14. Phrase structure • Paradigmatic relationships (e.g., constituency) • Syntagmatic relationships (e.g., collocations) S NP VP That man VBD PP NP butterfly NP the IN caught a net with

15. Phrase-structure grammars Peter gave Mary a book. Mary gave Peter a book. • Constituent order (SVO, SOV) • imperative forms • sentences with auxiliary verbs • interrogative sentences • declarative sentences • start symbol and rewrite rules • context-free view of language

16. Sample phrase-structure grammar S  NP VPNP  AT NNSNP  AT NNNP  NP PPVP  VP PPVP  VBDVP  VBD NPP  IN NP AT  theNNS  childrenNNS  studentsNNS  mountainsVBD  sleptVBD  ateVBD  sawIN  inIN  ofNN  cake

17. Phrase structure grammars • Local dependencies • Non-local dependencies • Subject-verb agreement The women who found the wallet were given a reward. • wh-extraction Should Peter buy a book? Which book should Peter buy? • Empty nodes

18. Dependency: arguments and adjuncts Sue watched the man at the next table. • Event + dependents (verb arguments are usually NPs) • agent, patient, instrument, goal - semantic roles • subject, direct object, indirect object • transitive, intransitive, and ditransitive verbs • active and passive voice

19. Subcategorization • Arguments: subject + complements • adjuncts vs. complements • adjuncts are optional and describe time, place, manner… • subordinate clauses • subcategorization frames

20. Subcategorization Subject: The children eat candy.Object: The children eat candy.Prepositional phrase: She put the book on the table.Predicative adjective: We made the man angry.Bare infinitive: She helped me walk.To-infinitive: She likes to walk.Participial phrase: She stopped singing that tune at the end.That-clause: She thinks that it will rain tomorrow.Question-form clauses: She asked me what book I was reading.

21. Subcategorization frames • Intransitive verbs: The woman walked • Transitive verbs: John loves Mary • Ditransitive verbs: Mary gave Peter flowers • Intransitive with PP: I rent in Paddington • Transitive with PP: She put the book on the table • Sentential complement: I know that she likes you • Transitive with sentential complement: She told me that Gary is coming on Tuesday

22. Selectional restrictions and preferences • Subcategorization frames capture syntactic regularities about complements • Selectional restrictions and preferences capture semantic regularities: bark, eat

23. Phrase structure ambiguity • Grammars are used for generating and parsing sentences • Parses • Syntactic ambiguity • Attachment ambiguity: Our company is training workers. • The children ate the cake with a spoon. • High vs. low attachment • Garden path sentences: The horse raced past the barn fell. Is the book on the table red?

24. Ungrammaticality vs. semantic abnormality * Slept children the.# Colorless green ideas sleep furiously.# The cat barked.

25. Semantics and pragmatics • Lexical semantics and compositional semantics • Hypernyms, hyponyms, antonyms, meronyms and holonyms (part-whole relationship, tire is a meronym of car), synonyms, homonyms • Senses of words, polysemous words • Homophony (bass). • Collocations: white hair, white wine • Idioms: to kick the bucket

26. Discourse analysis • Anaphoric relations: 1. Mary helped Peter get out of the car. He thanked her.2. Mary helped the other passenger out of the car. The man had asked her for help because of his foot injury. • Information extraction problems (entity crossreferencing) Hurricane Hugo destroyed 20,000 Florida homes.At an estimated cost of one billion dollars, the disasterhas been the most costly in the state’s history.

27. Pragmatics • The study of how knowledge about the world and language conventions interact with literal meaning. • Speech acts • Research issues: resolution of anaphoric relations, modeling of speech acts in dialogues

28. Other areas of NLP • Linguistics is traditionally divided into phonetics, phonology, morphology, syntax, semantics, and pragmatics. • Sociolinguistics: interactions of social organization and language. • Historical linguistics: change over time. • Linguistic typology • Language acquisition • Psycholinguistics: real-time production and perception of language

29. Word classes andpart-of-speech tagging

30. Part of speech tagging • Problems: transport, object, discount, address • More problems: content • French: est, président, fils • “Book that flight” – what is the part of speech associated with “book”? • POS tagging: assigning parts of speech to words in a text. • Three main techniques: rule-based tagging, stochastic tagging, transformation-based tagging

31. Rule-based POS tagging • Use dictionary or FST to find all possible parts of speech • Use disambiguation rules (e.g., ART+V) • Typically hundreds of constraints can be designed manually

32. Example in French <S> ^ beginning of sentence La rf b nms u article teneur nfs nms noun feminine singular Moyenne jfs nfs v1s v2s v3s adjective feminine singular en p a b preposition uranium nms noun masculine singular des p r preposition rivi`eres nfp noun feminine plural , x punctuation bien_que cs subordinating conjunction délicate jfs adjective feminine singular À p preposition calculer v verb

33. Sample rules BS3 BI1: A BS3 (3rd person subject personal pronoun) cannot be followed by a BI1 (1st person indirect personal pronoun). In the example: ``il nous faut'' ({\it we need}) - ``il'' has the tag BS3MS and ``nous'' has the tags [BD1P BI1P BJ1P BR1P BS1P]. The negative constraint ``BS3 BI1'' rules out ``BI1P'', and thus leaves only 4 alternatives for the word ``nous''. N K: The tag N (noun) cannot be followed by a tag K (interrogative pronoun); an example in the test corpus would be: ``... fleuve qui ...'' (...river, that...). Since ``qui'' can be tagged both as an ``E'' (relative pronoun) and a ``K'' (interrogative pronoun), the ``E'' will be chosen by the tagger since an interrogative pronoun cannot follow a noun (``N''). R V:A word tagged with R (article) cannot be followed by a word tagged with V (verb): for example ``l' appelle'' (calls him/her). The word ``appelle'' can only be a verb, but ``l''' can be either an article or a personal pronoun. Thus, the rule will eliminate the article tag, giving preference to the pronoun.

34. Stochastic POS tagging • HMM tagger • Pick the most likely tag for this word • P(word|tag) * P(tag|previous n tags) – find tag sequence that maximizes the probability formula • A bigram-based HMM tagger chooses the tag ti for word wi that is most probable given the previous tag ti-1 and the current word wi: • ti = argmaxj P(tj|ti-1,wi) • ti = argmaxj P(tj|ti-1)P(wi|tj) : HMM equation for a single tag

35. Example • Secretariat/NNP is/VBZ expected/VBN to/TO race/VB tomorrow/ADV • People/NNS continue/VBP to/TO inquire/VB the/DT reason/NN for/IN the/DT race/NN for/IN outer/JJ space/NN • P(VB|TO)P(race|VB) • P(NN|TO)P(race|NN) • TO: to+VB (to sleep), to+NN (to school)

36. Example (cont’d) • P(NN|TO) = .021 • P(VB|TO) = .34 • P(race|NN) = .00041 • P(race|VB) = .00003 • P(VB|TO)P(race|VB) = .00001 • P(NN|TO)P(race|NN) = .000007

37. HMM Tagging • T = argmax P(T|W), where T=t1,t2,…,tn • By Bayes’ rule: P(T|W) = P(T)P(W|T)/P(W) • Thus we are attempting to choose the sequence of tags that maximizes the rhs of the equation • P(W) can be ignored • P(T)P(W|T) = P(wi|w1t1…wi-1ti-1ti)P(ti|w1t1…wi-1ti-1)

38. Transformation-based learning • P(NN|race) = .98 • P(VB|race) = .02 • Change NN to VB when the previous tag is TO • Types of rules: • The preceding (following) word is tagged z • The word two before (after) is tagged z • One of the two preceding (following) words is tagged z • One of the three preceding (following) words is tagged z • The preceding word is tagged z and the following word is tagged w

39. Confusion matrix Most confusing: NN vs. NNP vs. JJ, VBD vs. VBN vs. JJ

40. Readings • J&M Chapters 1, 2, 3, 8 • “What is Computational Linguistics” by Hans Uszkoreithttp://www.coli.uni-sb.de/~hansu/what_is_cl.html • Lecture notes #1

41. Readings • J&M Chapters 3, 8 • Lecture notes #2