Paper: Domain Adaptation with Active Learning for Word Sense Disambiguation

ACL ID P07-1007
Title Domain Adaptation with Active Learning for Word Sense Disambiguation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

When a word sense disambiguation (WSD) system is trained on one domain but ap- plied to a different domain, a drop in ac- curacy is frequently observed. This high- lights the importance of domain adaptation for word sense disambiguation. In this pa- per, we first show that an active learning ap- proach can be successfully used to perform domain adaptation of WSD systems. Then, by using the predominant sense predicted by expectation-maximization (EM) and adopt- ing a count-merging technique, we improve the effectiveness of the original adaptation process achieved by the basic active learn- ing approach.