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

• Type: Journal
• Year: 2002
• Where: COLING
• Authors: Kengo Sato, Hiroaki Saito
• Keywords: COLING 2002, COLING 2008, Non-aligned Parallel Corpora, Parallel Corpora, Support Vector Machines