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The Role of Semantic Roles in Disambiguating Verb Senses

The Role of Semantic Roles in Disambiguating Verb Senses. Hoa Trang Dang and Martha Palmer 2005. Proceedings of the 43rd Annual Meeting of the ACL, pages 42–49.

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The Role of Semantic Roles in Disambiguating Verb Senses

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  1. The Role of Semantic Roles in Disambiguating Verb Senses • Hoa Trang Dang and Martha Palmer • 2005. Proceedings of the 43rd Annual Meeting of the ACL, pages 42–49.

  2. Verbs are syntactically complex, and their syntax is thought to be determined by their underlying semantics (Grimshaw, 1990; Levin, 1993). • Disambiguation of verb senses can be further improved with better extraction of semantic roles.

  3. Basic Automatic System • WordNet: word senses. • PropBank: semantic role labels. • Mallet: for learning maximum entropy models with Gaussian priors. • Senseval-2: the system was tested on thousands of the test instances of the 29 verbs from the English lexical sample task for Senseval-2.

  4. Basic Automatic System • Topical features • Local features • Collocation features • Syntactic features • Semantic features

  5. Topical Features • Topical features for a verb in a sentence look for the presence of keywords occurring anywhere in the sentence and any surrounding sentences provided as context. • The set of keywords is specific to each verb lemma to be disambiguated.

  6. Local Features • Collocational features: • unigrams: words w-2, w-1, w0, w+1,w+2 • part-of-speech p-2, p-1, p0, p+1,p+2 • bigrams: w-2w-1,w-1w+1,w+1w+2; p-2p-1,p-1p+1p+1p+2 • trigrams: w-3w-2w-1,w-2w-1w+1,w-1w+1w+2,w+1w+2w+3; • p-3p-2p-1, p-2p-1p+1, p-1p+1p+2, p+1p+2p+3

  7. Local Features • Syntactic features • Is the sentence passive? • Is there a subject, direct object, indirect object , or clausal complement? • What is the word (if any) that is the particle or head of the subject, direct object, or indirect object? • If there is a PP complement, what is the preposition, and what is the object of the preposition?

  8. Local Features • Semantic features: • What is the Named Entity tag (PERSON, ORGANIZATION, LOCATION, UNKNOWN) for each proper noun in the syntactic positions above? • What are the possible WordNet synsets and hypernyms for each noun in the syntactic positions above?

  9. Evaluation Accuracy of system on Senseval-2 verbs using topical features and different subsets of local features. co=collocational syn=syntactic sem=semantic This system: 62.5% accuracy. Lee and Ng, 2002: 61.1% accuracy.

  10. Evaluation Accuracy of system on Senseval-2 verbs, using topical features and different subsets of semantic class features. ne=named entity tags wn=WordNet classes

  11. PropBank • PropBank is a corpus in which verbs are annotated with semantic tags, including coarse-grained sense distinctions and predicate-argument structures. • Example: [ ARG0 Mr. Bush] has [rel called] [ ARG1-for for an agreement by next September at the latest]

  12. Frameset Tagging Accuracy of system on frameset-tagging task for verbs with more than one frameset, using different types of local features. (pb=PropBank role features.) *The most frequent frameset gives a baseline accuracy of 76.0%.

  13. WordNet Sense-tagging Accuracy of system on WordNet sense-tagging for instances in both Senseval-2 and PropBank, using different types of local features. *PropBank ARGM features are included.

  14. Frameset tags for WordNet sense-tagging Accuracy of system on WordNet sensetagging of 20 Senseval-2 verbs with more than one frameset. orig=original local features.

  15. Conclusion • Disambiguation for verbs can be improved through more accurate extraction of features representing information such as that contained in the framesets and predicate argument structures annotated in PropBank.

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