Word Sense Disambiguation using case based Approach with Minimal Features Set
Journal Title: Indian Journal of Computer Science and Engineering - Year 2011, Vol 2, Issue 4
Abstract
In this paper we presented a case based approach for word sense disambiguation using minimal features set. To make the disambiguation, we took only two features for two different methods, post-bigram (immediate left word with ambiguous word – l1w) and pre-bigram (ambiguous word with immediate right word of it – wr1). To classify the cases for disambiguation, we followed three steps: instance or case identification from the distributed dictionaries, instance filtering based on the PoS and finally, case selection based on similarity measuring methods. Here, we used three different distant measuring functions, Euclidean, city-block and cosine methods. Set of reduced form of cases are treated as input for disambiguation. For making disambiguation, we applied K-nearest neighboring algorithm and artificial Neural Network. Among these two, KNN produced better disambiguation accuracy of 81.75% from cosine cases using pre-bigram features.
Authors and Affiliations
Tamilselvi P , S. K. Srivatsa
Keywords
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