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  1. LING 138/238 SYMBSYS 138Intro to Computer Speech and Language Processing Lecture 16: Lexical Semantics, Wordnet, etc November 23, 2004 Dan Jurafsky Thanks to Jim Martin for many of these slides! LING 138/238 Autumn 2004

  2. Meaning • Traditionally, meaning in language has been studied from three perspectives • The meanings of individual words • How those meanings combine to make meanings for individual sentences or utterances • How those meanings combine to make meanings for a text or discourse • We are going to focus today on word meaning, also called lexical semantics. LING 138/238 Autumn 2004

  3. Outline: Lexical Semantics • Concepts about word meaning including: • Homonymy, Polysemy, Synonymy • Thematic roles • Two computational areas • Enabling resource: • WordNet • Enabling technology: • Word sense disambiguation LING 138/238 Autumn 2004

  4. Preliminaries • What’s a word? • Definitions we’ve used over the quarter: Types, tokens, stems, roots, inflected forms, etc... • Lexeme: An entry in a lexicon consisting of a pairing of a form with a single meaning representation • Lexicon: A collection of lexemes LING 138/238 Autumn 2004

  5. Relationships between word meanings • Homonymy • Polysemy • Synonymy • Antonymy • Hypernomy • Hyponomy • Meronomy LING 138/238 Autumn 2004

  6. Homonymy • Homonymy: • Lexemes that share a form • Phonological, orthographic or both • But have unrelated, distinct meanings • Clear example: • Bat (wooden stick-like thing) vs • Bat (flying scary mammal thing) • Or bank (financial institution) versus bank (riverside) • Can be homophones, homographs, or both: • Homophones: • Write and right • Piece and peace LING 138/238 Autumn 2004

  7. Homonymy causes problems for NLP applications • Text-to-Speech • Same orthographic form but different phonological form • bass vs bass • Information retrieval • Different meanings same orthographic form • QUERY: bat care • Machine Translation • Speech recognition • Why? LING 138/238 Autumn 2004

  8. Polysemy • The bankis constructed from red brickI withdrew the money from the bank • Are those the same sense? • Or consider the following WSJ example • While some banks furnish sperm only to married women, others are less restrictive • Which sense of bank is this? • Is it distinct from (homonymous with) the river bank sense? • How about the savings bank sense? LING 138/238 Autumn 2004

  9. Polysemy • A single lexeme with multiple related meanings (bank the building, bank the finantial institution) • Most non-rare words have multiple meanings • The number of meanings is related to its frequency • Verbs tend more to polysemy • Distinguishing polysemy from homonymy isn’t always easy (or necessary) LING 138/238 Autumn 2004

  10. Metaphor and Metonymy • Specific types of polysemy • Metaphor: • Germany will pull Slovenia out of its economic slump. • I spent 2 hours on that homework. • Metonymy • The White House announced yesterday. • This chapter talks about part-of-speech tagging • Bank (building) and bank (financial institution) LING 138/238 Autumn 2004

  11. How do we know when a word has more than one sense? • ATIS examples • Which flights serve breakfast? • Does America West serve Philadelphia? • The “zeugma” test: • ?Does United serve breakfast and San Jose? LING 138/238 Autumn 2004

  12. Synonyms • Word that have the same meaning in some or all contexts. • filbert hazelnut • youth adolescent • big large • automobile car • Two lexemes are synonyms if they can be successfully substituted for each other in all situations LING 138/238 Autumn 2004

  13. Synonyms • But there are few (or no) examples of perfect synonymy. • Why should that be? • Even if many aspects of meaning are identical • Still may not preserve the acceptability based on notions of politeness, slang, register, genre, etc. • Example: • Big and large? • That’s my big sister • That’s my large sister LING 138/238 Autumn 2004

  14. Antonyms • Words that are opposites with respect to one feature of their meaning • Otherwise, they are very similar! • Dark light • Boy girl • Hot cold • Up down • In out LING 138/238 Autumn 2004

  15. Word Similarity Computation • For various computational applications it’s useful to find words which are similar to another word. • Machine translation (to find near-synonyms) • Information retrieval (to do “query expansion”) • Two ways to do this: • Automatic computation based on distributional similarity • Lsa.colorado.edu • Use a thesaurus which lists similar words. • WordNet (we’ll return to this in a few slides) LING 138/238 Autumn 2004

  16. Hyponymy • Hyponymy: the meaning of one lexeme is a subset of the meaning of another • Since dogs are canids • Dog is a hyponym of canid and • Canid is a hypernym of dog • Similarly, • Car is a hyponym of vehicle • Vehicle is a hypernym of car LING 138/238 Autumn 2004

  17. WordNet • A hierarchically organized lexical database • On-line thesaurus + aspects of a dictionary • Versions for other languages are under development LING 138/238 Autumn 2004

  18. WordNet • Demo: • http://www.cogsci.princeton.edu/cgi-bin/webwn LING 138/238 Autumn 2004

  19. Format of Wordnet Entries LING 138/238 Autumn 2004

  20. WordNet Noun Relations LING 138/238 Autumn 2004

  21. WordNet Verb and Adj Relations LING 138/238 Autumn 2004

  22. WordNet Hierarchies LING 138/238 Autumn 2004

  23. How is “sense” defined in WordNet? • The critical thing to grasp about WordNet is the notion of a synset; it’s their version of a sense or a concept • Example: table as a verb to mean defer • > {postpone, hold over, table, shelve, set back, defer, remit, put off} • For WordNet, the meaning of this sense of tableis this list. • Another example: give has 45 senses in WN; one of them is: {supply, provide, render, furnish}. LING 138/238 Autumn 2004

  24. The Internal Structure of Words • Previous section talked about relations between words • Now we’ll look at “internal structure” of word meaning. We’ll look at only a few aspects: • Thematic roles in predicate-bearing lexemes • Selection restrictions on thematic roles • Decompositional semantics of predicates • Feature-structures for nouns LING 138/238 Autumn 2004

  25. Thematic Roles • Thematic roles: • Thematic roles are semantic generalizations over the specific roles that occur with specific verbs. • I.e. Takers, givers, eaters, makers, doers, killers, all have something in common • -er • They’re all the agents of the actions • We can generalize across other roles as well to come up with a small finite set of such roles LING 138/238 Autumn 2004

  26. Thematic Roles LING 138/238 Autumn 2004

  27. Thematic Role Examples LING 138/238 Autumn 2004

  28. Thematic Roles • Takes some of the work away from the verbs. • It’s not the case that every verb is unique and has to completely specify how all of its arguments uniquely behave. • Provides a locus for organizing semantic processing • It permits us to distinguish near surface-level semantics from deeper semantics LING 138/238 Autumn 2004

  29. Linking • Thematic roles, syntactic categories and their positions in larger syntactic structures are all intertwined in complicated ways. For example… • AGENTS are often subjects • In a VP->V NP NP rule, the first NP is often a GOAL and the second a THEME LING 138/238 Autumn 2004

  30. More on Linking • John opened the door • AGENT THEME • The door was opened by John • THEME AGENT • The door opened • THEME • John opened the door with the key • AGENT THEME INSTRUMENT LING 138/238 Autumn 2004

  31. Inference • Given an event expressed by a verb that expresses a transfer, what can we conclude about the thing labeled THEME with respect to the thing labeled GOAL? LING 138/238 Autumn 2004

  32. Deeper Semantics • From the WSJ… • He melted her reserve with a husky-voiced paean to her eyes. • If we label the constituents He and her reserve as the Melter and Melted, then those labels lose any meaning they might have had. • If we make them Agent and Theme then we can do more inference. LING 138/238 Autumn 2004

  33. Problems • What exactly is a role? • What’s the right set of roles? • Are such roles universals? • Are these roles atomic? • I.e. Agents • Animate, Volitional, Direct causers, etc • Can we automatically label syntactic constituents with thematic roles? LING 138/238 Autumn 2004

  34. Selection Restrictions • I want to eat someplace near campus • Using thematic roles we can now say that eat is a predicate that has an AGENT and a THEME • What else? • And that the AGENT must be capable of eating and the THEME must be something typically capable of being eaten LING 138/238 Autumn 2004

  35. As Logical Statements • For eat… • Eating(e) ^Agent(e,x)^ Theme(e,y)^Isa(y, Food) (adding in all the right quantifiers and lambdas) LING 138/238 Autumn 2004

  36. Back to WordNet • Use WordNet hyponyms (type) to encode the selection restrictions LING 138/238 Autumn 2004

  37. Selectional Restrictions as loose approximation of deeper semantics • Unfortunately, verbs are polysemous and language is creative… WSJ examples… • … ate glass on an empty stomach accompanied only by water and tea • you can’t eat gold for lunch if you’re hungry • … get it to try to eat Afghanistan LING 138/238 Autumn 2004

  38. Solutions • Eat glass • This is actually about an eating event, so might change our model of “Edible”. • Eat gold • Also about eating, and the can’t creates a scope that permits the THEME to not be edible • Eat Afghanistan • This is harder, its not really about eating at all LING 138/238 Autumn 2004

  39. Word Sense Disambiguation (WSD) • Given a word in context, decide which sense of the word this is. LING 138/238 Autumn 2004

  40. Selection Restrictions for WSD • Semantic selection restrictions can be used to help in WSD. • They can help disambiguate: • Ambiguous arguments to unambiguous predicates • Ambiguous predicates with unambiguous arguments • Ambiguity all around LING 138/238 Autumn 2004

  41. WSD and Selection Restrictions • Ambiguous arguments • Prepare a dish • Wash a dish • Ambiguous predicates • Serve Denver • Serve breakfast • Both • Serves vegetarian dishes LING 138/238 Autumn 2004

  42. Problems • As we saw earlier, selection restrictions are violated all the time. • This doesn’t mean that the sentences are ill-formed or preferred less than others. • This approach needs some way of categorizing and dealing with the various ways that restrictions can be violated LING 138/238 Autumn 2004

  43. Supervised Machine Learning Approaches • Supervised machine learning approach: • a training corpus of words tagged in context with their sense • used to train a classifier that can tag words in new text • Just as we saw for part-of-speech tagging, speech recognition, statistical MT. LING 138/238 Autumn 2004

  44. WSD Tags • What’s a tag? • A dictionary sense? • For example, for WordNet an instance of “bass” in a text has 8 possible tags or labels (bass1 through bass8). LING 138/238 Autumn 2004

  45. WordNet Bass The noun ``bass'' has 8 senses in WordNet • bass - (the lowest part of the musical range) • bass, bass part - (the lowest part in polyphonic music) • bass, basso - (an adult male singer with the lowest voice) • sea bass, bass - (flesh of lean-fleshed saltwater fish of the family Serranidae) • freshwater bass, bass - (any of various North American lean-fleshed freshwater fishes especially of the genus Micropterus) • bass, bass voice, basso - (the lowest adult male singing voice) • bass - (the member with the lowest range of a family of musical instruments) • bass -(nontechnical name for any of numerous edible marine and freshwater spiny-finned fishes) LING 138/238 Autumn 2004

  46. Representations • Most supervised ML approaches require a very simple representation for the input training data. • Vectors of sets of feature/value pairs • I.e. files of comma-separated values • So our first task is to extract training data from a corpus with respect to a particular instance of a target word • This typically consists of a characterization of the window of text surrounding the target LING 138/238 Autumn 2004

  47. Representations • This is where ML and NLP intersect • If you stick to trivial surface features that are easy to extract from a text, then most of the work is in the ML system • If you decide to use features that require more analysis (say parse trees) then the ML part may be doing less work (relatively) if these features are truly informative LING 138/238 Autumn 2004

  48. Surface Representations • Collocational and co-occurrence information • Collocational • Features about words at specific positions near target word • Often limited to just word identity and POS • Co-occurrence • Features about words that occur anywhere in the window (regardless of position) • Typically limited to frequency counts LING 138/238 Autumn 2004

  49. Examples • Example text (WSJ) • An electric guitar and bass player stand off to one side not really part of the scene, just as a sort of nod to gringo expectations perhaps • Assume a window of +/- 2 from the target LING 138/238 Autumn 2004

  50. Examples • Example text • An electric guitar andbassplayer stand off to one side not really part of the scene, just as a sort of nod to gringo expectations perhaps • Assume a window of +/- 2 from the target LING 138/238 Autumn 2004