Word sense disambiguation using a second language monolingual corpus
Computational Linguistics
Translating collocations for bilingual lexicons: a statistical approach
Computational Linguistics
Foundations of statistical natural language processing
Foundations of statistical natural language processing
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
An IR approach for translating new words from nonparallel, comparable texts
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
An algorithm for finding noun phrase correspondences in bilingual corpora
ACL '93 Proceedings of the 31st 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
PRINCIPAR: an efficient, broad-coverage, principle-based parser
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Learning bilingual collocations by word-level sorting
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Word translation disambiguation using bilingual bootstrapping
Computational Linguistics
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
Collocation translation acquisition using monolingual corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A multi-stage chinese collocation extraction system
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
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Bilingual collocation correspondence is helpful to machine translation and second language learning. Existing techniques for identifying Chinese-English collocation correspondence suffer from two major problems. They are sensitive to the coverage of the bilingual dictionary and the insensitive to semantic and contextual information. This paper presents the ICT (Improved Collocation Translation) method to overcome these problems. For a given Chinese collocation, the word translation candidates extracted from a bilingual dictionary are expanded to improve the coverage. A new translation model, which incorporates statistics extracted from monolingual corpora, word semantic similarities from monolingual thesaurus and bilingual context similarities, is employed to estimate and rank the probabilities of the collocation correspondence candidates. Experiments show that ICT is robust to the coverage of bilingual dictionary. It achieves 50.1% accuracy for the first candidate and 73.1% accuracy for the top-3 candidates.