Estimating and exploiting the entropy of sense distributions

Authors:
Peng Jin;Diana McCarthy;Rob Koeling;John Carroll
Affiliations:
Peking University, Beijing, China;University of Sussex, Falmer, East Sussex, UK;University of Sussex, Falmer, East Sussex, UK;University of Sussex, Falmer, East Sussex, UK
Venue:
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Year:
2009

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Abstract

Word sense distributions are usually skewed. Predicting the extent of the skew can help a word sense disambiguation (WSD) system determine whether to consider evidence from the local context or apply the simple yet effective heuristic of using the first (most frequent) sense. In this paper, we propose a method to estimate the entropy of a sense distribution to boost the precision of a first sense heuristic by restricting its application to words with lower entropy. We show on two standard datasets that automatic prediction of entropy can increase the performance of an automatic first sense heuristic.