Word sense disambiguation using a second language monolingual corpus
Computational Linguistics
Estimating Word Translation Probabilities from Unrelated Monolingual Corpora Using the EM Algorithm
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A method for word sense disambiguation of unrestricted text
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
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Word sense disambiguation has sense division and sense selection as its two sub-problems. An appropriate solution to the sense division problem is usually dependent on the application being pursued. In the context of machine translation, picking the correct translation for a word among multiple candidates, is known as target word selection. The work in this paper uses the Web as the main knowledge source to address the difficulty of making a target word selection based on statistics, which are normally drawn from rather limited corpora. The proposed approach uses simple and easily accessible web statistics–search engine hits (number of document returned for a particular query) to demonstrate the great potential of the Web as a knowledge source for word sense disambiguation. Our experimental results so far are very encouraging.