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Estimating and exploiting the entropy of sense distributions

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posted on 2023-06-08, 11:21 authored by Peng Jin, Diana McCarthy, Rob Koeling, John Carroll
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.

History

Publication status

  • Published

Page range

233-236

Presentation Type

  • paper

Event name

Proceedings of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT) 2009 Conference: Short Papers

Event location

Boulder, Colorado

Event type

conference

Department affiliated with

  • Informatics Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2012-04-24

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