Improving Feature Selection for Maximum Entropy-Based Word Sense Disambiguation

Authors:
Armando Suárez;Manuel Palomar
Affiliations:
-;-
Venue:
PorTAL '02 Proceedings of the Third International Conference on Advances in Natural Language Processing
Year:
2002

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Abstract

In this paper, an evaluation of several feature selections for word sense disambiguation is presented. The method used to classify linguistic contexts in its correct sense is based on maximum entropy probability models. In order to study their relevance for each word, several types of features have been analyzed for a few words selected from the DSO corpus. An improved definition of features in order to increase efficiency is presented as well.