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Fine-grained and Coarse-grained Word Sense Disambiguation

Fine-grained and Coarse-grained Word Sense Disambiguation. Jinying Chen, Hoa Trang Dang, Martha Palmer August 22, 2003. Outline. Maxent Word Sense Disambiguator Coarse-grained WSD by Decision Tree Future Work. Maxent Word Sense Disambiguator (Martha, Hoa, Christiane, 2002 ).

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Fine-grained and Coarse-grained Word Sense Disambiguation

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  1. Fine-grained and Coarse-grained Word Sense Disambiguation Jinying Chen, Hoa Trang Dang, Martha Palmer August 22, 2003

  2. Outline • Maxent Word Sense Disambiguator • Coarse-grained WSD by Decision Tree • Future Work

  3. Maxent Word Sense Disambiguator(Martha, Hoa, Christiane, 2002 ) • A deterministic model producing probability fj(sense,context):binary features : weight of feature j Z(context) : normalizing factor • Can combine evidence from different knowledge source • Feature weights determined automatically (GIS)

  4. Maxent Word Sense Disambiguator Features used in Maxent Model for English WSD • Local Contextual Predicates • Collocational features, e.g., target verb w, pos of w; pos of words at position –1,+1, w.r.t. w; words at positions –2, -1, +1, +2, w.r.t. w • Systactic features, e.g., active vs. passive, is there a sentential complement, subj, obj or indirect obj etc. • Semantic features, e.g., Named Entity tag (PER, ORG, LOC) for proper nouns, and WN synsets and hypernyms for all nouns in above syntactic relation to w • Topical Contextual Keywords • Select 200-300 words k with lowest entropy (P(sense|k)), i.e., being most informative, from anywhere in context

  5. Maxent Word Sense Disambiguator Fine-grained and coarse-grained WSD • Part of the results from (Martha, Hoa, Christiane, 2002 ) Table 1 The Performance of Maxent Word Sense Disambiguator on five verbs

  6. Coarse-grained WSD by Decision Tree • A simpler model compared with Maxent Model • Using Semantic Features from PropBank • PropBank • Each verb is defined by several framesets • All verb instances belonging to the same frameset share a common set of roles • Roles can be argn (n=0,1,…) and argM-f • Frameset is consistent with Verb Sense Group • Frameset tags and roles are semantic features for VSG

  7. Automatic Verb Sense Grouping Coarse-grained WSD by Decision Tree Building Decision Tree • Use c5.0 of DT • 3 Feature Sets: • SF (Simple Feature set) works best: • VOICE: PAS, ACT • FRAMESET: 01,02, … • ARGn (n=0,1,2 …) : 0(not occur), 1(occur) • CoreFrame: 01-ARG0-ARG1, 02-ARG0-ARG2,… • ARGM: 0(has not ARGM), 1(has ARGM) • ARGM-f(f=DIS, ADV, …): i (occur i times)

  8. Coarse-grained WSD by Decision Tree Experimental Results Table 2 Error rate of Decision Tree on five verbs

  9. Coarse-grained WSD by Decision Tree Discussion • Simple feature set and simple DT algorithms works well • Potential sparse data problem • Complicate DT algorithms (e.g., with boosting) tend to overfit the data • Complex features are not utilized by the model • Solution: use large corpus, e.g., parsed BNC corpus without frameset annotation

  10. Future Work • Train DT or other models for coarse-grained WSD on large corpus without frameset annotation • Unsupervised Frameset Tagging by EM-clustering • Clustering nouns automatically instead of using WordNet to group nouns

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