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Attempting to Model Sense Division for Word Sense Disambiguation
Abstract
This chapter starts exploring the potential of co-occurrence data for word sense disambiguation. The findings on the robustness of the different distribution of co-occurrence data on the assumption that distinct meanings of the same word attract different co-occurrence data, has taken the author to experiment (i) on possible grouping of word meanings by means of cluster analysis and (ii) on word sense disambiguation using discriminant function analysis. In addition, two priorities have been pursued: first, find robust statistical techniques, and second, minimize computational costs. Future research aims at the transition from coarse-grained senses to finer-grained ones by means of reiteration of the same model on different levels of contextual differentiation.
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