Instance based learning with automatic feature selection applied to word sense disambiguation

Authors:
Rada Mihalcea
Affiliations:
University of North Texas, Denton, TX
Venue:
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Year:
2002

Citing 10
Cited 11

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Abstract

We describe an algorithm for Word Sense Disambiguation (WSD) that relies on a lazy learner improved with automatic feature selection. The algorithm was implemented in a system that achieves excellent performance on the set of data released during the SENSEVAL-2 competition. We present the results obtained and discuss the performance of various features in the context of supervised learning algorithms for WSD.