JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
The Use of MSVM and HMM for Sentence Alignment |
| 영문제목(English Title) |
The Use of MSVM and HMM for Sentence Alignment |
| 저자(Author) |
Mohamed Abdel Fattah
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| 원문수록처(Citation) |
VOL 08 NO. 02 PP. 0301 ~ 0314 (2012. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English 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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| 키워드(Keyword) |
Sentence Alignment
English/ Arabic Parallel Corpus
Parallel Corpora
Machine Translation
Multi-Class Support Vector Machine
Hidden Markov model
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| 파일첨부 |
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