Thu, 21 Aug 2008 15:12:37 BST CiteULike: briordan Huynh http://www.citeulike.org/user/briordan/author/Huynh CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/briordan/article/2786162 <i>Data & Knowledge Engineering, Vol. 63, No. 2. (November 2007), pp. 381-396.</i><br /><br />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. Combining classifiers for word sense disambiguation based on Dempster-Shafer theory and OWA operators Cuong Le Van-Nam Huynh Akira Shimazu Yoshiteru Nakamori doi:10.1016/j.datak.2007.03.013 Data & Knowledge Engineering, Vol. 63, No. 2. (November 2007), pp. 381-396. 2008-05-12T02:03:21-00:00 2007 Data & Knowledge Engineering 63 2 381 396 computational-lexical-semantics distributional-similarity http://www.citeulike.org/user/briordan/article/2786146 <i>Computer Speech & Language, Vol. 22, No. 4. (October 2008), pp. 330-345.</i><br /><br />Word sense disambiguation (WSD) is the problem of determining the right sense of a polysemous word in a certain context. This paper investigates the use of unlabeled data for WSD within a framework of semi-supervised learning, in which labeled data is iteratively extended from unlabeled data. Focusing on this approach, we first explicitly identify and analyze three problems inherently occurred piecemeal in the general bootstrapping algorithm; namely the imbalance of training data, the confidence of new labeled examples, and the final classifier generation; all of which will be considered integratedly within a common framework of bootstrapping. We then propose solutions for these problems with the help of classifier combination strategies. This results in several new variants of the general bootstrapping algorithm. Experiments conducted on the English lexical samples of Senseval-2 and Senseval-3 show that the proposed solutions are effective in comparison with previous studies, and significantly improve supervised WSD. Semi-supervised learning integrated with classifier combination for word sense disambiguation Anh-Cuong Le Akira Shimazu Van-Nam Huynh Le-Minh Nguyen doi:10.1016/j.csl.2007.11.001 Computer Speech & Language, Vol. 22, No. 4. (October 2008), pp. 330-345. 2008-05-12T01:57:49-00:00 2008 Computer Speech & Language 22 4 330 345 computational-linguistics