Instance based learning with automatic feature selection applied to word sense disambiguation
Rada F. Mihalcea · 2002
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.