Paper: Estimating Class Priors In Domain Adaptation For Word Sense Disambiguation

ACL ID P06-1012
Title Estimating Class Priors In Domain Adaptation For Word Sense Disambiguation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

Instances of a word drawn from different domains may have different sense priors (the proportions of the different senses of a word). This in turn affects the accuracy of word sense disambiguation (WSD) sys- tems trained and applied on different do- mains. This paper presents a method to estimate the sense priors of words drawn from a new domain, and highlights the im- portance of using well calibrated probabil- ities when performing these estimations. By using well calibrated probabilities, we are able to estimate the sense priors effec- tively to achieve significant improvements in WSD accuracy.