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Lexical Semantics & Word Sense Disambiguation

Lexical Semantics & Word Sense Disambiguation. Ling571 Deep Processing Techniques for NLP February 16, 2011. Roadmap. Lexical semantics Lexical taxonomy WordNet Thematic Roles Issues Resources: PropBank & FrameNet Selectional Restrictions Primitive decompositions. WordNet Taxonomy.

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Lexical Semantics & Word Sense Disambiguation

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  1. Lexical Semantics & Word Sense Disambiguation Ling571 Deep Processing Techniques for NLP February 16, 2011

  2. Roadmap • Lexical semantics • Lexical taxonomy • WordNet • Thematic Roles • Issues • Resources: • PropBank& FrameNet • Selectional Restrictions • Primitive decompositions

  3. WordNet Taxonomy • Most widely used English sense resource • Manually constructed lexical database • 3 Tree-structured hierarchies • Nouns (117K) , verbs (11K), adjective+adverb (27K) • Entries: synonym set, gloss, example use • Relations between entries: • Synonymy: in synset • Hypo(per)nym: Isa tree

  4. WordNet

  5. Noun WordNet Relations

  6. WordNet Taxonomy

  7. Thematic Roles • Describe semantic roles of verbal arguments • Capture commonality across verbs

  8. Thematic Roles • Describe semantic roles of verbal arguments • Capture commonality across verbs • E.g. subject of break, open is AGENT • AGENT: volitional cause • THEME: things affected by action

  9. Thematic Roles • Describe semantic roles of verbal arguments • Capture commonality across verbs • E.g. subject of break, open is AGENT • AGENT: volitional cause • THEME: things affected by action • Enables generalization over surface order of arguments • JohnAGENT broke the windowTHEME

  10. Thematic Roles • Describe semantic roles of verbal arguments • Capture commonality across verbs • E.g. subject of break, open is AGENT • AGENT: volitional cause • THEME: things affected by action • Enables generalization over surface order of arguments • JohnAGENT broke the windowTHEME • The rockINSTRUMENTbroke the windowTHEME

  11. Thematic Roles • Describe semantic roles of verbal arguments • Capture commonality across verbs • E.g. subject of break, open is AGENT • AGENT: volitional cause • THEME: things affected by action • Enables generalization over surface order of arguments • JohnAGENT broke the windowTHEME • The rockINSTRUMENTbroke the windowTHEME • The windowTHEME was broken by JohnAGENT

  12. Thematic Roles • Thematic grid, θ-grid, case frame • Set of thematic role arguments of verb

  13. Thematic Roles • Thematic grid, θ-grid, case frame • Set of thematic role arguments of verb • E.g. Subject:AGENT; Object:THEME, or • Subject: INSTR; Object: THEME

  14. Thematic Roles • Thematic grid, θ-grid, case frame • Set of thematic role arguments of verb • E.g. Subject:AGENT; Object:THEME, or • Subject: INSTR; Object: THEME • Verb/Diathesis Alternations • Verbs allow different surface realizations of roles

  15. Thematic Roles • Thematic grid, θ-grid, case frame • Set of thematic role arguments of verb • E.g. Subject:AGENT; Object:THEME, or • Subject: INSTR; Object: THEME • Verb/Diathesis Alternations • Verbs allow different surface realizations of roles • DorisAGENTgave the bookTHEME to CaryGOAL

  16. Thematic Roles • Thematic grid, θ-grid, case frame • Set of thematic role arguments of verb • E.g. Subject:AGENT; Object:THEME, or • Subject: INSTR; Object: THEME • Verb/Diathesis Alternations • Verbs allow different surface realizations of roles • DorisAGENTgave the bookTHEME to CaryGOAL • DorisAGENT gave CaryGOAL the bookTHEME

  17. Thematic Roles • Thematic grid, θ-grid, case frame • Set of thematic role arguments of verb • E.g. Subject:AGENT; Object:THEME, or • Subject: INSTR; Object: THEME • Verb/Diathesis Alternations • Verbs allow different surface realizations of roles • DorisAGENTgave the bookTHEME to CaryGOAL • DorisAGENT gave CaryGOAL the bookTHEME • Group verbs into classes based on shared patterns

  18. Canonical Roles

  19. Thematic Role Issues • Hard to produce

  20. Thematic Role Issues • Hard to produce • Standard set of roles • Fragmentation: Often need to make more specific • E,g, INSTRUMENTS can be subject or not

  21. Thematic Role Issues • Hard to produce • Standard set of roles • Fragmentation: Often need to make more specific • E,g, INSTRUMENTS can be subject or not • Standard definition of roles • Most AGENTs: animate, volitional, sentient, causal • But not all…. • Strategies: • Generalized semantic roles: PROTO-AGENT/PROTO-PATIENT • Defined heuristically: PropBank • Define roles specific to verbs/nouns: FrameNet

  22. Thematic Role Issues • Hard to produce • Standard set of roles • Fragmentation: Often need to make more specific • E,g, INSTRUMENTS can be subject or not • Standard definition of roles • Most AGENTs: animate, volitional, sentient, causal • But not all….

  23. Thematic Role Issues • Hard to produce • Standard set of roles • Fragmentation: Often need to make more specific • E,g, INSTRUMENTS can be subject or not • Standard definition of roles • Most AGENTs: animate, volitional, sentient, causal • But not all…. • Strategies: • Generalized semantic roles: PROTO-AGENT/PROTO-PATIENT • Defined heuristically: PropBank

  24. Thematic Role Issues • Hard to produce • Standard set of roles • Fragmentation: Often need to make more specific • E,g, INSTRUMENTS can be subject or not • Standard definition of roles • Most AGENTs: animate, volitional, sentient, causal • But not all…. • Strategies: • Generalized semantic roles: PROTO-AGENT/PROTO-PATIENT • Defined heuristically: PropBank • Define roles specific to verbs/nouns: FrameNet

  25. PropBank • Sentences annotated with semantic roles • Penn and Chinese Treebank

  26. PropBank • Sentences annotated with semantic roles • Penn and Chinese Treebank • Roles specific to verb sense • Numbered: Arg0, Arg1, Arg2,… • Arg0: PROTO-AGENT; Arg1: PROTO-PATIENT, etc

  27. PropBank • Sentences annotated with semantic roles • Penn and Chinese Treebank • Roles specific to verb sense • Numbered: Arg0, Arg1, Arg2,… • Arg0: PROTO-AGENT; Arg1: PROTO-PATIENT, etc • E.g. agree.01 • Arg0: Agreer

  28. PropBank • Sentences annotated with semantic roles • Penn and Chinese Treebank • Roles specific to verb sense • Numbered: Arg0, Arg1, Arg2,… • Arg0: PROTO-AGENT; Arg1: PROTO-PATIENT, etc • E.g. agree.01 • Arg0: Agreer • Arg1: Proposition

  29. PropBank • Sentences annotated with semantic roles • Penn and Chinese Treebank • Roles specific to verb sense • Numbered: Arg0, Arg1, Arg2,… • Arg0: PROTO-AGENT; Arg1: PROTO-PATIENT, etc • E.g. agree.01 • Arg0: Agreer • Arg1: Proposition • Arg2: Other entity agreeing

  30. PropBank • Sentences annotated with semantic roles • Penn and Chinese Treebank • Roles specific to verb sense • Numbered: Arg0, Arg1, Arg2,… • Arg0: PROTO-AGENT; Arg1: PROTO-PATIENT, etc • E.g. agree.01 • Arg0: Agreer • Arg1: Proposition • Arg2: Other entity agreeing • Ex1: [Arg0The group] agreed [Arg1it wouldn’t make an offer]

  31. FrameNet • Semantic roles specific to Frame • Frame: script-like structure, roles (frame elements)

  32. FrameNet • Semantic roles specific to Frame • Frame: script-like structure, roles (frame elements) • E.g. change_position_on_scale: increase, rise • Attribute, Initial_value, Final_value

  33. FrameNet • Semantic roles specific to Frame • Frame: script-like structure, roles (frame elements) • E.g. change_position_on_scale: increase, rise • Attribute, Initial_value, Final_value • Core, non-core roles

  34. FrameNet • Semantic roles specific to Frame • Frame: script-like structure, roles (frame elements) • E.g. change_position_on_scale: increase, rise • Attribute, Initial_value, Final_value • Core, non-core roles • Relationships b/t frames, frame elements • Add causative: cause_change_position_on_scale

  35. Selectional Restrictions • Semantic type constraint on arguments • I want to eat someplace close to UW

  36. Selectional Restrictions • Semantic type constraint on arguments • I want to eat someplace close to UW • E.g. THEME of eating should be edible • Associated with senses

  37. Selectional Restrictions • Semantic type constraint on arguments • I want to eat someplace close to UW • E.g. THEME of eating should be edible • Associated with senses • Vary in specificity:

  38. Selectional Restrictions • Semantic type constraint on arguments • I want to eat someplace close to UW • E.g. THEME of eating should be edible • Associated with senses • Vary in specificity: • Imagine: AGENT: human/sentient; THEME: any

  39. Selectional Restrictions • Semantic type constraint on arguments • I want to eat someplace close to UW • E.g. THEME of eating should be edible • Associated with senses • Vary in specificity: • Imagine: AGENT: human/sentient; THEME: any • Representation: • Add as predicate in FOL event representation

  40. Selectional Restrictions • Semantic type constraint on arguments • I want to eat someplace close to UW • E.g. THEME of eating should be edible • Associated with senses • Vary in specificity: • Imagine: AGENT: human/sentient; THEME: any • Representation: • Add as predicate in FOL event representation • Overkill computationally; requires large commonsense KB

  41. Selectional Restrictions • Semantic type constraint on arguments • I want to eat someplace close to UW • E.g. THEME of eating should be edible • Associated with senses • Vary in specificity: • Imagine: AGENT: human/sentient; THEME: any • Representation: • Add as predicate in FOL event representation • Overkill computationally; requires large commonsense KB • Associate with WordNetsynset (and hyponyms)

  42. Primitive Decompositions • Jackendoff(1990), Dorr(1999), McCawley (1968) • Word meaning constructed from primitives • Fixed small set of basic primitives • E.g. cause, go, become, • kill=cause X to become Y

  43. Primitive Decompositions • Jackendoff(1990), Dorr(1999), McCawley (1968) • Word meaning constructed from primitives • Fixed small set of basic primitives • E.g. cause, go, become, • kill=cause X to become Y • Augment with open-ended “manner” • Y = not alive • E.g. walk vsrun

  44. Primitive Decompositions • Jackendoff(1990), Dorr(1999), McCawley (1968) • Word meaning constructed from primitives • Fixed small set of basic primitives • E.g. cause, go, become, • kill=cause X to become Y • Augment with open-ended “manner” • Y = not alive • E.g. walk vs run • Fixed primitives/Infinite descriptors

  45. Word Sense Disambiguation • SelectionalRestriction-based approaches • Limitations • Robust Approaches • Supervised Learning Approaches • Naïve Bayes • Dictionary-based Approaches • Bootstrapping Approaches • One sense per discourse/collocation • Unsupervised Approaches • Schutze’s word space • Resource-based Approaches • Dictionary parsing, WordNet Distance • Why they work • Why they don’t

  46. Word Sense Disambiguation • Application of lexical semantics • Goal: Given a word in context, identify the appropriate sense • E.g. plants and animals in the rainforest • Crucial for real syntactic & semantic analysis

  47. Word Sense Disambiguation • Application of lexical semantics • Goal: Given a word in context, identify the appropriate sense • E.g. plants and animals in the rainforest • Crucial for real syntactic & semantic analysis • Correct sense can determine • .

  48. Word Sense Disambiguation • Application of lexical semantics • Goal: Given a word in context, identify the appropriate sense • E.g. plants and animals in the rainforest • Crucial for real syntactic & semantic analysis • Correct sense can determine • Available syntactic structure • Available thematic roles, correct meaning,..

  49. Selectional Restriction Approaches • Integrate sense selection in parsing and semantic analysis – e.g. with lambda calculus • Concept: Predicate selects sense • Washing dishes vs stir-frying dishes

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