Estimating and exploiting the entropy of sense distributions

Jin, Peng, McCarthy, Diana, Koeling, Rob and Carroll, John (2009) Estimating and exploiting the entropy of sense distributions. In: Proceedings of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT) 2009 Conference: Short Papers, Boulder, Colorado.

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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.

Item Type: Conference or Workshop Item (Paper)
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: T Technology
Related URLs:
Depositing User: Juan Loera Gonzalez
Date Deposited: 24 Apr 2012 09:47
Last Modified: 24 Apr 2012 09:47
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