@inproceedings{e1313878a1fd415e99f8916ee6d7d0f1,
title = "Estimating and exploiting the entropy of sense distributions",
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.",
author = "Peng Jin and Diana McCarthy and Rob Koeling and John Carroll",
note = "Publisher Copyright: {\textcopyright} 2009 Association for Computational Linguistics; 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2009 ; Conference date: 31-05-2009 Through 05-06-2009",
year = "2009",
language = "Ingl{\'e}s",
series = "NAACL-HLT 2009 - Human Language Technologies: 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Short Papers",
publisher = "Association for Computational Linguistics (ACL)",
pages = "233--236",
editor = "Mari Ostendorf and Michael Collins and Shri Narayanan and Oard, {Douglas W.} and Lucy Vanderwende",
booktitle = "NAACL-HLT 2009 - Human Language Technologies",
address = "Estados Unidos",
}