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.
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Niu, ZY., Ji, D., Tan, CL., Yang, L. (2005). Word Sense Disambiguation by Semi-supervised Learning. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2005. Lecture Notes in Computer Science, vol 3406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30586-6_25
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DOI: https://doi.org/10.1007/978-3-540-30586-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24523-0
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