Extracting Word Sequence Correspondences with Support Vector Machines

Extracting Word Sequence Correspondences with Support Vector Machines Kengo SATO and Hiroaki SAITO Department of Information and Computer Science Keio University 3–14–1, Hiyoshi, Kohoku, Yokohama 223–8522, Japan {satoken,hxs}@nak.ics.keio.ac.jp Abstract 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 generaliza- tion, rarely cause over-fit for training samples and can learn dependencies of features by using a kernel function. Our method uses features for the trans- lation 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 % re- call rate of the extracted word sequence correspon- dences. This demonstrates that our method could reduce the cost for making translation dictionaries. 1 Introduction Translation dictionaries used in multilingual natu- ral language processing such as machine transla- tion have been made manually, but a great deal of labor is required for this work and it is difficult to keep the description of the dictionaries consis- tent. Therefore, researches of extracting transla- tion pairs from parallel corpora automatically be- come active recently (Gale and Church, 1991; Kaji and Aizono, 1996; Tanaka and Iwasaki, 1996; Kita- mura and Matsumoto, 1996; Fung, 1997; Melamed, 1997; Sato and Nakanishi, 1998). This paper proposes a learning and extract- ing method of bilingual word sequence correspon- dences from non-aligned parallel corpora with Sup- port Vector Machines (SVMs) (Vapnik, 1999). SVMs are ones of large margin classifiers (Smola et al., 2000) which are based on the strategy where margins between separating boundary and vectors of which elements express the features of train- ing samples is maximized. Therefore, SVMs have higer ability of the generalization than other learn- ing models such as the decision trees and rarely cause over-fit for training samples. In addition, by using kernel functions, they can learn non-linear separating boundary and dependencies between the features. Therefore, SVMs have been recently used for the natural language processing such as text categorization (Joachims, 1998; Taira and Haruno, 1999), chunk identification (Kudo and Matsumoto, 2000b), dependency structure analysis (Kudo and Matsumoto, 2000a). The method proposed in this paper does not re- quire aligned parallel corpora which do not exist too many at present. Therefore, without limiting appli- cable domains, word sequence correspondences can been extracted. 2 Support Vector Machines SVMs are binary classifiers which linearly separate d dimension vectors to two classes. Each vector rep- resents the sample which has d features. It is distin- guished whether given sample ⃗x = (x1, x2, . . . , xd) belongs to X1 or X2 by equation (1) : f(⃗x) = sign(g(⃗x)) = � 1 �⃗x ∈ X1 � −1 �⃗x ∈ X2 � (1) where g(⃗x) is the hyperplain which separates two classes in which ⃗w and b are decided by optimiza- tion. g(⃗x) = ⃗w · ⃗x + b (2) Let supervise signals for the training samples be expressed …

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