Word sense disambiguation using a second language monolingual corpus
Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Word translation disambiguation using bilingual bootstrapping
Computational Linguistics
Word sense disambiguation by learning from unlabeled data
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Semi-supervised training of a kernel PCA-based model for word sense disambiguation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Corpus-Based Extraction of Collocations in Chinese
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
| Hi-index | 0.00 |
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.