Natural Language Processing COMPSCI 423/723 Rohit Kate

1. Natural Language ProcessingCOMPSCI 423/723 Rohit Kate

2. Lexical Semantics and Word Sense Disambiguation Some of the slides have been adapted from Raymond Mooney’s NLP course at UT Austin.

3. Basic Steps of Natural Language Processing Phonetics Words Syntactic processing Parses Semantic processing Meaning Pragmatic processing Sound waves Meaning in context

4. Lexical Semantics • Study of meanings of words • How to represent word meanings? • How are they related to each others? • Synonyms, antonyms, hypernyms (more general) , hyponyms (more specific) • A word can have multiple meanings “I am going to the bank.” • Compositionality: How meanings of individual words combine to give meaning of a sentence • Many exceptions: “kick the bucket”

5. Lexeme, Lexicon & Lemma • Lexeme: Smallest unit of language which has a meaning (roughly dictionary entry), e.g. run • Takes various inflected word forms, e.g. runs, running, ran • conduct (verb) is a different lexeme from conduct (noun) • Lexicon: A finite set of lexemes (roughly dictionary) • Lemma: The canonical or basic form that represents the lexeme, e.g. run

6. Lemmatization • The process of mapping word forms to their lemmas, e.g. running => run • Typically done using morphological analysis • Often done in NLP to avoid data sparsity, but depending on the application sometimes it may be best to keep the word forms

7. Lemmatization is not Trivial • May depend on the context • He found the ball => find • He will found the Institute => found • Depends on the part of speech • He conducted the orchestra => conduct (verb)

8. Stemming • Reduce a word to its “stem” • Relates to lemmatization but the stem need not be a word itself • May reduce compute, computational, computing all to comput • The purpose of stemming is to bring variant forms of a word together, not to map a word onto its canonical form • Porter’s stemmer is a well known simple algorithm for stemming that is mostly based on removing well known suffixes • Less linguistically motivated and less effective but is easy to do and often serves the purpose

9. Word Senses • A word sense is a particular meaning of a word • Senses of a word may be entirely different with no relations, called homonyms • Bank: money bank, river bank • Senses of a word may be related, called polysemes • Bank: financial institute, building of the financial institute, storage of blood (blood bank) • No hard threshold to distinguish between polysemy and homonymy, it’s a matter of degree

10. When I use a word it means just what I choose it to mean - neither more nor less.

11. How Many Senses a Word Has? • Not always an easy question • Drive the car • Drive to school • Drive me mad

12. How Many Senses a Word Has? • Dictionaries (or humans) may differ on how many senses a word has • Typically dictionaries or linguistic resources give very fine-grained senses of a word, but for NLP that may not be needed (in fact that may hurt) • WordNet has 34 senses for drive

13. Relations Between Senses • Synonyms: When two senses of two words are identical or very similar, e.g. buy & purchase • Could be tested by substitution • I bought/purchased a car. • Probably there is no perfect synonymy, they still may be different in some contexts, e.g. water and H2O • Synonymy is best defined for senses not words • Home purchase is a long process. • *Home buy is a long process.

14. Relations Between Senses • Antonyms: Senses of words with opposite meanings, e.g. long/short, rise/fall • While antonyms are very different because they have opposite meanings, they are also very similar because they share all other aspects, e.g. long and short are degree of lengths • It is often difficult to distinguish between synonyms and antonyms if automatically extracted from a corpus using measures of context similarity • This is good. • This is nice. • This is bad.

15. Relations Between Senses • Hyponyms: A sense of a word is more specific than a sense of another word, e.g. apple is a hyponym of fruit • Hypernyms: Opposite of hyponym, e.g. fruit is a hypernym of apple • Meronyms: Part-whole relation, e.g. wheel is a meronym of car • Holonyms: Opposite of meronyms, e.g. car is a holonym of wheel

16. WordNet A computational resource for English sense relations, lexical database Available for free, browse or download: http://wordnet.princeton.edu/ Developed by famous cognitive psychologist George Miller and a team at Princeton University Database of word senses and their relations 16

17. WordNet • Synset (synonym set): Set of near synonyms in WordNet • Basic primitive of WordNet • Each synset expresses a semantic concept • Example synset: {drive, thrust, driving force} • Entry for each word shows all the synsets (senses) the word appears in, some description and sometimes example usage • About 140,000 words and 109,000 synsets • Synsets (not individual words) are connected by various sense relations

18. Some WordNet Synset Relationships Antonym: front  back Similar: unquestioning  absolute Cause: kill  die Entailment: breathe  inhale Holonym: chapter  text (part-of) Meronym: computer  cpu (whole-of) Hyponym: tree  plant (specialization) Hypernym: fruit  apple (generalization) 18

19. A WordNet Snapshot motor vehicle, automotive vehicle synsets hypernym car, auto, automobile, machine,motorcar accelerator, gas pedal, gas meronym hyponym hyponym cab, taxi, taxicab, hack ambulance

20. WordNets for Other Languages EuroWordNet: Individual WordNets for some European languages (Dutch, Italian, Spanish, German, French, Czech, and Estonia) which are also interconnected by interlingual links http://www.illc.uva.nl/EuroWordNet/ WordNets for some asian languages: Hindi: http://www.cfilt.iitb.ac.in/wordnet/webhwn/ Marathi: http://www.cfilt.iitb.ac.in/wordnet/webmwn/ Japanese: http://nlpwww.nict.go.jp/wn-ja/index.en.html 20

21. WordNet Senses WordNets senses (like many dictionary senses) tend to be very fine-grained “play” as a verb has 35 senses, including play a role or part: “Gielgud played Hamlet” pretend to have certain qualities or state of mind: “John played dead.” Difficult to disambiguate to this level for people and computers. Only expert lexicographers are perhaps able to reliably differentiate senses Not clear such fine-grained senses are useful for NLP Several proposals for grouping senses into coarser, easier to identify senses (e.g. homonyms only) 21

22. Word Sense Disambiguation (WSD) • Task of automatically selecting the correct sense for a word • Many tasks in NLP require disambiguation of ambiguous words • Question Answering • Information Retrieval • Machine Translation • Text Mining • Phone Help Systems • Understanding how people disambiguate words is an interesting problem that can provide insight in psycholinguistics

23. WSD Tasks • Lexical sample task: • Choose one or more ambiguous words each with a sense inventory • Disambiguate occurrences of those specific words in a corpus • All words task: • In a corpus disambiguate every word with a a sense tag from a broad-coverage lexical database (e.g. WordNet).

24. Supervised Learning for WSD • Treat as a classification problem with the potential senses for the target word as the classification labels • Decide appropriate features and a classification method (Naïve Bayes, MaxEnt, decision lists etc.) • Train using data labeled with the correct word senses • Use the trained classifier to disambiguate instances of the target word in the test corpus

25. Feature Engineering • The success of machine learning requires instances to be represented using an effective set of features that are correlated with the categories of interest • Feature engineering can be a laborious process that requires substantial human expertise and knowledge of the domain • In NLP it is common to extract many (even thousands of) potentially features and use a learning algorithm that works well with many relevant and irrelevant features

26. Contextual Features • Surrounding bag of words • POS of neighboring words • Local collocations • Syntactic relations Experimental evaluations indicate that all of these features are useful; and the best results comes from integrating all of these cues in the disambiguation process.

27. Surrounding Bag of Words • Unordered individual words near the ambiguous word. • Words in the same sentence. • May include words in the previous sentence or surrounding paragraph. • Gives general topical cues of the context. • May use feature selection to determine a smaller set of words that help discriminate possible senses. • May just remove common “stop words” such as articles, prepositions, etc.

28. POS of Neighboring Words • POS of the word narrows down the senses • Also use part-of-speech of immediately neighboring words. • Provides evidence of local syntactic context. • P-i is the POS of the word i positions to the left of the target word. • Pi is the POS of the word i positions to the right of the target word. • Typical to include features for: P-3, P-2, P-1, P1, P2, P3

29. Local Collocations • Specific lexical context immediately adjacent to the word. • For example, to determine if “interest” as a noun refers to “readiness to give attention” or “money paid for the use of money”, the following collocations are useful: • “in the interest of” • “an interest in” • “interest rate” • “accrued interest” • Ci,j is a feature of the sequence of words from local position i to j relative to the target word. • C-2,1 for “in the interest of” is “in the of” • Typical to include: • Single word context: C-1,-1 , C1,1, C-2,-2, C2,2 • Two word context: C-2,-1,C-1,1 ,C1,2 • Three word context: C-3,-1, C-2,1, C-1,2, C1,3

30. Syntactic Relations(Ambiguous Verbs) • For an ambiguous verb, it is very useful to know its direct object. • “played the game” • “played the guitar” • “played the risky and long-lasting card game” • “played the beautiful and expensive guitar” • “played the big brass tuba at the football game” • “played the game listening to the drums and the tubas” • May also be useful to know its subject: • “The game was played while the band played.” • “The game that included a drum and a tuba was played on Friday.”

31. Syntactic Relations(Ambiguous Nouns) • For an ambiguous noun, it is useful to know what verb it is an object of: • “played the piano and the horn” • “wounded by the rhinoceros’ horn” • May also be useful to know what verb it is the subject of: • “the bank near the river loaned him $100” • “the bank is eroding and the bank has given the city the money to repair it”

32. Syntactic Relations(Ambiguous Adjectives) • For an ambiguous adjective, it useful to know the noun it is modifying. • “a brilliant young man” • “a brilliant yellow light” • “a wooden writing desk” • “a wooden acting performance”

33. S NP VP ProperN V NP John played DET N piano the Using Syntax in WSD • Produce a parse tree for a sentence using a syntactic parser. • For ambiguous verbs, use the head word of its direct object and of its subject as features. • For ambiguous nouns, use verbs for which it is the object and the subject as features. • For ambiguous adjectives, use the head word (noun) of its NP as a feature.

34. Feature Vectors A small example

35. Classification Play? Naïve Bayes … C1 P-1 P1

36. Evaluation of WSD • “In vitro”: • Corpus developed in which one or more ambiguous words are labeled with explicit sense tags according to some sense inventory. • Corpus used for training and testing WSD and evaluated using accuracy (percentage of labeled words correctly disambiguated). • Use most common sense selection as a baseline. • “In vivo”: • Incorporate WSD system into some larger application system, such as machine translation, information retrieval, or question answering. • Evaluate relative contribution of different WSD methods by measuring performance impact on the overall system on final task (accuracy of MT, IR, or QA results).

37. Evaluating Categorization • Evaluation must be done on test data that are independent of the training data (usually a disjoint set of instances). • Classification accuracy: c/n where n is the total number of test instances and c is the number of test instances correctly classified by the system. • Results can vary based on sampling error due to different training and test sets. • Average results over multiple training and test sets (splits of the overall data) for the best results.

38. N-Fold Cross-Validation • Ideally, test and training sets are independent on each trial. • But this would require too much labeled data. • Partition data into N equal-sized disjoint segments. • Run N trials, each time using a different segment of the data for testing, and training on the remaining N1 segments. • This way, at least test-sets are independent. • Report average classification accuracy over the N trials. • Typically, N = 10.

39. Learning Curves • In practice, labeled data is usually rare and expensive. • Would like to know how performance varies with the number of training instances. • Learning curves plot classification accuracy on independent test data (Y axis) versus number of training examples (X axis).

40. N-Fold Learning Curves • Want learning curves averaged over multiple trials. • Use N-fold cross validation to generate N full training and test sets. • For each trial, train on increasing fractions of the training set, measuring accuracy on the test data for each point on the desired learning curve.

41. WSD “line” Corpus • Example WSD corpus • 4,149 examples from newspaper articles containing the word “line.” • Each instance of “line” labeled with one of 6 senses from WordNet. • Each example includes a sentence containing “line” and the previous sentence for context.

42. Senses of “line” • Product: “While he wouldn’t estimate the sale price, analysts have estimated that it would exceed $1 billion. Kraft also told analysts it plans to develop and test a line of refrigerated entrees and desserts, under the Chillery brand name.” • Formation: “C-LD-R L-V-S V-NNA reads a sign in Caldor’s book department. The 1,000 or so people fighting for a place in line have no trouble filling in the blanks.” • Text: “Newspaper editor Francis P. Church became famous for a 1897 editorial, addressed to a child, that included the line “Yes, Virginia, there is a Santa Clause.” • Cord: “It is known as an aggressive, tenacious litigator. Richard D. Parsons, a partner at Patterson, Belknap, Webb and Tyler, likes the experience of opposing Sullivan & Cromwell to “having a thousand-pound tuna on the line.” • Division: “Today, it is more vital than ever. In 1983, the act was entrenched in a new constitution, which established a tricameral parliament along racial lines, whith separate chambers for whites, coloreds and Asians but none for blacks.” • Phone: “On the tape recording of Mrs. Guba's call to the 911 emergency line, played at the trial, the baby sitter is heard begging for an ambulance.”

43. Experimental Data for WSD of “line” • Sample equal number of examples of each sense to construct a corpus of 2,094. • Represent as simple binary vectors of word occurrences in 2 sentence context. • Stop words eliminated • Stemmed to eliminate morphological variation • Final examples represented with 2,859 binary word features.

44. Learning Curves for WSD of “line” [Mooney, 1996]

45. Discussion of Learning Curves for WSD of “line” • Naïve Bayes and Perceptron give the best results. • Both use a weighted linear combination of evidence from many features. • Symbolic systems that try to find a small set of relevant features tend to overfit the training data and are not as accurate. • Nearest neighbor method that weights all features equally is also not as accurate. • Of symbolic systems, decision lists work the best.

46. SenseEval • Standardized international “competition” on WSD http://www.senseval.org/ • Organized by the Association for Computational Linguistics (ACL) Special Interest Group on the Lexicon (SIGLEX). • Competitions: • Senseval 1: 1998 • Senseval 2: 2001 • Senseval 3: 2004 • Under SemEval 1: 2007 • Under SemEval 2: 2010

47. Other Approaches to WSD • Dictionary based methods • Lesk algorithm: Choose the sense whose dictionary gloss shares the most words with the context • Semi-supervised learning • Bootstrap from a small number of labeled examples to exploit unlabeled data • Train a classifier on the labeled data • Test on the unlabeled data and treat the instances with high confidence of disambiguation as (weakly) labeled • Iterate till no more high confidence instances can be found • Exploit “one sense per discourse”

48. Issues in WSD • What is the right granularity of a sense inventory? • Integrating WSD with other NLP tasks • Syntactic parsing • Semantic role labeling • Semantic parsing • Does WSD actually improve performance on some real end-user task? • Information retrieval • Information extraction • Machine translation • Question answering

49. Homework 5 • Which is the right WordNet synset (sense) according to you for the word “position” in the sentence: “He kept on debating but his position was not clear.”? What feature(s) will be useful to decide that by a WSD system?