Combining classifiers for word sense disambiguation based on Dempster-Shafer theory and OWA operators

Authors:
Cuong Anh Le;Van-Nam Huynh;Akira Shimazu;Yoshiteru Nakamori
Affiliations:
Japan Advanced Institute of Science and Technology 1-1 Asahidai, Tatsunokuchi, Ishikawa 923-1292, Japan;Japan Advanced Institute of Science and Technology 1-1 Asahidai, Tatsunokuchi, Ishikawa 923-1292, Japan;Japan Advanced Institute of Science and Technology 1-1 Asahidai, Tatsunokuchi, Ishikawa 923-1292, Japan;Japan Advanced Institute of Science and Technology 1-1 Asahidai, Tatsunokuchi, Ishikawa 923-1292, Japan
Venue:
Data & Knowledge Engineering
Year:
2007

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Abstract

In this paper, we discuss a framework for weighted combination of classifiers for word sense disambiguation (WSD). This framework is essentially based on Dempster-Shafer theory of evidence [G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, 1976] and ordered weighted averaging (OWA) operators [R.R. Yager, On ordered weighted averaging aggregation operators in multicriteria decision making, IEEE Transactions on Systems, Man, and Cybernetics 18 (1988) 183-190] We first determine various kinds of features which could provide complementarily linguistic information for the context, and then combine these sources of information based on Dempster's rule of combination and OWA operators for identifying the meaning of a polysemous word. We experimentally design a set of individual classifiers, each of which corresponds to a distinct representation type of context considered in the WSD literature, and then the discussed combination strategies are tested and compared on English lexical samples of Senseval-2 and Senseval-3.