Domain Adaptation with Active Learning for Word Sense Disambiguation

By using the predominant sense predicted by expectation-maximization (EM) and adopting a count-merging technique, this paper improves the effectiveness of the original adaptation process achieved by the basic active learning approach.

Abstract

When a word sense disambiguation (WSD) system is trained on one domain but applied to a different domain, a drop in accuracy is frequently observed. This highlights the importance of domain adaptation for word sense disambiguation. In this paper, we first show that an active learning approach can be successfully used to perform domain adaptation of WSD systems. Then, by using the predominant sense predicted by expectation-maximization (EM) and adopting a count-merging technique, we improve the effectiveness of the original adaptation process achieved by the basic active learning approach.