Word association norms, mutual information, and lexicography
Computational Linguistics
Identifying word correspondence in parallel texts
HLT '91 Proceedings of the workshop on Speech and Natural Language
A word-to-word model of translational equivalence
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
An IR approach for translating new words from nonparallel, comparable texts
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
A pattern matching method for finding noun and proper noun translations from noisy parallel corpora
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Extracting word correspondences from bilingual corpora based on word co-occurrences information
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Extraction of lexical translations from non-aligned corpora
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Automatic identification of word translations from unrelated English and German corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Unsupervised word sense disambiguation using bilingual comparable corpora
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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Two methods using comparable corpora to select translation equivalents appropriate to a domain were devised and evaluated. The first method ranks translation equivalents of a target word according to similarity of their contexts to that of the target word. The second method ranks translation equivalents according to the ratio of associated words that suggest them. An experiment using the EDR bilingual dictionary together with Wall Street Journal and Nihon Keizai Shimbun corpora proved that the method using the ratio of associated words outperforms the method based on contextual similarity. Namely, in a quantitative evaluation using pseudo words, the maximum F-measure of the former method was 86%, while that of the latter method was 82%.