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|Title:||Domain adaptation with active learning forword sense disambiguation||Authors:||Chan, Y.S.
|Issue Date:||2007||Citation:||Chan, Y.S.,Ng, H.T. (2007). Domain adaptation with active learning forword sense disambiguation. ACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics : 49-56. ScholarBank@NUS Repository.||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. © 2007 Association for Computational Linguistics.||Source Title:||ACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics||URI:||http://scholarbank.nus.edu.sg/handle/10635/40502||ISBN:||9781932432862|
|Appears in Collections:||Staff Publications|
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