Extracting Word Sequence Correspondences with Support Vector Machines


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.

19th International Conference on Computational Linguistics, COLING 2002, pp. 870–876, Howard International House and Academia Sinica, Taipei, Taiwan, August 24 - September 1, 2002