Estimating class priors in domain adaptation for word sense disambiguation

By using well calibrated probabilities, this paper is able to estimate the sense priors of words drawn from a new domain effectively to achieve significant improvements in WSD accuracy.

Abstract

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) systems trained and applied on different domains. This paper presents a method to estimate the sense priors of words drawn from a new domain, and highlights the importance of using well calibrated probabilities when performing these estimations. By using well calibrated probabilities, we are able to estimate the sense priors effectively to achieve significant improvements in WSD accuracy.