Paper: Estimating and Exploiting the Entropy of Sense Distributions

ACL ID N09-2059
Title Estimating and Exploiting the Entropy of Sense Distributions
Venue Human Language Technologies
Session Short Paper
Year 2009
Authors

Word sense distributions are usually skewed. Predicting the extent of the skew can help a word sense disambiguation (WSD) system de- termine whether to consider evidence from the local context or apply the simple yet effec- tive 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 in- crease the performance of an automatic first sense heuristic.