Abstract
Arguing that various ways of using context in word sense disambiguation (WSD) can be considered as distinct representations of a polysemous word, a theoretical framework for the weighted combination of soft decisions generated by experts employing these distinct representations is proposed in this paper. Essentially, this approach is based on the Dempster-Shafer theory of evidence. By taking the confidence of individual classifiers into account, a general rule of weighted combination for classifiers is formulated, and then two particular combination schemes are derived. These proposed strategies are experimentally tested on the datasets for four polysemous words, namely interest, line, serve, and hard.
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Le, C.A., Huynh, VN., Shimazu, A. (2005). An Evidential Reasoning Approach to Weighted Combination of Classifiers for Word Sense Disambiguation. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_51
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DOI: https://doi.org/10.1007/11510888_51
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