Text-based English-Arabic sentence alignment

Authors:
Mohamed Abdel Fattah;Fuji Ren;Shingo Kuroiwa
Affiliations:
Faculty of Engineering, University of Tokushima, Tokushima, Japan;Faculty of Engineering, University of Tokushima, Tokushima, Japan and School of Information Engineering, Beijing University of Posts & Telecommunications, Beijing, China;Faculty of Engineering, University of Tokushima, Tokushima, Japan
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

Citing 3
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Abstract

In this paper, we present a new approach to align sentences in bilingual parallel corpora based on the use of the linguistic information of the text pair in Gaussian mixture model (GMM) classifier. A feature parameter vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, cognate score and a bilingual lexicon extracted from the parallel corpus under consideration. A set of manually prepared training data has been assigned to train the Gaussian mixture model. Another set of data was used for testing. Using the Gaussian mixture model approach, we could achieve error reduction of 160% over length based approach when applied on English-Arabic parallel documents. In addition, the results of (GMM) outperform the results of the combined model which exploits length, punctuation, cognate and bilingual lexicon in a dynamic framework.