The Use of MSVM and HMM for Sentence Alignment

Mohamed Abdel Fattah
Volume: 8, No: 2, Page: 301 ~ 314, Year: 2012
10.3745/JIPS.2012.8.2.301
Keywords: Sentence Alignment, English/ Arabic Parallel Corpus, Parallel Corpora, Machine Translation, Multi-Class Support Vector Machine, Hidden Markov model
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Abstract
In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on the Multi-Class Support Vector Machine (MSVM) and the Hidden Markov Model (HMM) classifiers are presented. A feature vector is extracted from the text pair that is under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the Multi-Class Support Vector Machine and Hidden Markov Model. Another set of data was used for testing. The results of the MSVM and HMM outperform the results of the length based approach. Moreover these new approaches are valid for any language pairs and are quite flexible since the feature vector may contain less, more, or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research

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Cite this article
IEEE Style
Mohamed Abdel Fattah, "The Use of MSVM and HMM for Sentence Alignment," Journal of Information Processing Systems, vol. 8, no. 2, pp. 301~314, 2012. DOI: 10.3745/JIPS.2012.8.2.301.

ACM Style
Mohamed Abdel Fattah, "The Use of MSVM and HMM for Sentence Alignment," Journal of Information Processing Systems, 8, 2, (2012), 301~314. DOI: 10.3745/JIPS.2012.8.2.301.