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|Title:||Word sense disambiguation with semi-supervised learning||Authors:||Pham, T.P.
|Issue Date:||2005||Citation:||Pham, T.P.,Ng, H.T.,Lee, W.S. (2005). Word sense disambiguation with semi-supervised learning. Proceedings of the National Conference on Artificial Intelligence 3 : 1093-1098. ScholarBank@NUS Repository.||Abstract:||Current word sense disambiguation (WSD) systems based on supervised learning are still limited in that they do not work well for all words in a language. One of the main reasons is the lack of sufficient training data. In this paper, we investigate the use of unlabeled training data for WSD, in the framework of semi-supervised learning. Four semi-supervised learning algorithms are evaluated on 29 nouns of Senseval-2 (SE2) English lexical sample task and SE2 English all-words task. Empirical results show that unlabeled data can bring significant improvement in WSD accuracy. Copyright © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.||Source Title:||Proceedings of the National Conference on Artificial Intelligence||URI:||http://scholarbank.nus.edu.sg/handle/10635/41431|
|Appears in Collections:||Staff Publications|
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