Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41229
Title: Word sense disambiguation by semi-supervised learning
Authors: Niu, Z.-Y.
Ji, D.
Tan, C.-L. 
Yang, L.
Issue Date: 2005
Source: Niu, Z.-Y.,Ji, D.,Tan, C.-L.,Yang, L. (2005). Word sense disambiguation by semi-supervised learning. Lecture Notes in Computer Science 3406 : 238-241. ScholarBank@NUS Repository.
Abstract: In this paper we propose to use a semi-supervised learning algorithm to deal with word sense disambiguation problem. We evaluated a semi-supervised learning algorithm, local and global consistency algorithm, on widely used benchmark corpus for word sense disambiguation. This algorithm yields encouraging experimental results. It achieves better performance than orthodox supervised learning algorithm, such as kNN, and its performance on monolingual benchmark corpus is comparable to a state of the art bootstrapping algorithm (bilingual bootstrapping) for word sense disambiguation. © Springer-Verlag Berlin Heidelberg 2005.
Source Title: Lecture Notes in Computer Science
URI: http://scholarbank.nus.edu.sg/handle/10635/41229
ISSN: 03029743
Appears in Collections:Staff Publications

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