Text-Based English-Arabic Sentence Alignment

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.

Authors and Affiliations

1. Faculty of Engineering, University of Tokushima, 2-1 Minamijosanjima, Tokushima, 770-8506, Japan

   Mohamed Abdel Fattah, Fuji Ren & Shingo Kuroiwa

2. School of Information Engineering, Beijing University of Posts & Telecommunications, Beijing, 100088, China

   Fuji Ren

Editors and Affiliations

1. Institute of Intelligent Machines , Chinese Academy of Sciences , Anhui, Hefei, China

   De-Shuang Huang

2. Queen’s University , Belfast, UK

   Kang Li  & George William Irwin  & 

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