Word sense disambiguation using a second language monolingual corpus
Computational Linguistics
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Similarity-based word sense disambiguation
Computational Linguistics - Special issue on word sense disambiguation
Natural Language Engineering
Term-list translation using mono-lingual word co-occurrence vectors
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Identifying word translations in non-parallel texts
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Corpus-dependent association thesauri for information retrieval
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Extraction of lexical translations from non-aligned corpora
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Word sense acquisition from bilingual comparable corpora
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Bilingual-dictionary adaptation to domains
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Localized factor models for multi-context recommendation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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An unsupervised method for word sense disambiguation using a bilingual comparable corpus was developed. First, it extracts statistically significant pairs of related words from the corpus of each language. Then, aligning pairs of related words translingually, it calculates the correlation between the senses of a first-language polysemous word and the words related to the polysemous word, which can be regarded as clues for determining the most suitable sense. Finally, for each instance of the polysemous word, it selects the sense that maximizes the score, i.e., the sum of the correlations between each sense and the clues appearing in the context of the instance. To overcome both the problem of ambiguity in the translingual alignment of pairs of related words and that of disparity of topical coverage between corpora of different languages, an algorithm for calculating the correlation between senses and clues iteratively was devised. An experiment using Wall Street Journal and Nihon Keizai Shimbun corpora showed that the new method has promising performance; namely, the applicability and precision of its sense selection are 88.5% and 77.7%, respectively, averaged over 60 test polysemous words.