The Role of Semantic Roles in Disambiguating Verb Senses

Proceedings of the 43rd Annual Meeting of the ACL, pages 42–49, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics The Role of Semantic Roles in Disambiguating Verb Senses Hoa Trang Dang National Institute of Standards and Technology Gaithersburg, MD 20899 hoa.dang@nist.gov Martha Palmer Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 mpalmer@cis.upenn.edu Abstract We describe an automatic Word Sense Disambiguation (WSD) system that dis- ambiguates verb senses using syntactic and semantic features that encode infor- mation about predicate arguments and se- mantic classes. Our system performs at the best published accuracy on the English verbs of Senseval-2. We also experiment with using the gold-standard predicate- argument labels from PropBank for dis- ambiguating fine-grained WordNet senses and course-grained PropBank framesets, and show that disambiguation of verb senses can be further improved with bet- ter extraction of semantic roles. 1 Introduction A word can have different meanings depending on the context in which it is used. Word Sense Disambiguation (WSD) is the task of determining the correct meaning (“sense”) of a word in con- text, and several efforts have been made to develop automatic WSD systems. Early work on WSD (Yarowsky, 1995) was successful for easily distin- guishable homonyms like bank, which have multi- ple unrelated meanings. While homonyms are fairly tractable, highly polysemous verbs, which have re- lated but subtly distinct senses, pose the greatest challenge for WSD systems (Palmer et al., 2001). Verbs are syntactically complex, and their syntax is thought to be determined by their underlying se- mantics (Grimshaw, 1990; Levin, 1993). Levin verb classes, for example, are based on the ability of a verb to occur in pairs of syntactic frames (diathe- sis alternations); different senses of a verb belong to different verb classes, which have different sets of syntactic frames that are supposed to reflect under- lying semantic components that constrain allowable arguments. If this is true, then the correct sense of a verb should be revealed (at least partially) in its arguments. In this paper we show that the performance of automatic WSD systems can be improved by us- ing richer linguistic features that capture informa- tion about predicate arguments and their semantic classes. We describe our approach to automatic WSD of verbs using maximum entropy models to combine information from lexical collocations, syn- tax, and semantic class constraints on verb argu- ments. The system performs at the best published accuracy on the English verbs of the Senseval-2 (Palmer et al., 2001) exercise on evaluating au- tomatic WSD systems. The Senseval-2 verb in- stances have been manually tagged with their Word- Net sense and come primarily from the Penn Tree- bank WSJ. The WSJ corpus has also been manually annotated for predicate arguments as part of Prop- Bank (Kingsbury and Palmer, 2002), and the inter- section of PropBank and Senseval-2 forms a corpus containing gold-standard annotations of WordNet senses and PropBank semantic role labels. This pro- vides a unique opportunity to investigate the role of predicate arguments in verb sense disambiguation. …

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