Extracting word sequence correspondences with support vector machines
Kengo Sato, Hiroaki Saito · 2002
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.