Identifying word correspondence in parallel texts
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AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
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AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
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This paper proposes a learning and extracting method of word sequence correspondences from non-aligned parallel corpora with Support Vector Machines, which have high ability of the generalization, rarely cause over-fit for training samples and can learn dependencies of features by using a kernel function. Our method uses features for the translation model which use the translation dictionary, the number of words, part-of-speech, constituent words and neighbor words. Experiment results in which Japanese and English parallel corpora are used archived 81.1% precision rate and 69.0% recall rate of the extracted word sequence correspondences. This demonstrates that our method could reduce the cost for making translation dictionaries.