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English–Vietnamese cross-language paraphrase identification using hybrid feature classes
- Source :
- Journal of Heuristics. 28:193-209
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
Abstract
- Paraphrase identification plays an important role with various applications in natural language processing tasks such as machine translation, bilingual information retrieval, plagiarism detection, etc. With the development of information technology and the Internet, the requirement of textual comparing is not only in the same language but also in many different language pairs. Especially in Vietnamese, detecting paraphrase in the English–Vietnamese pair of sentences is a high demand because English is one of the most popular foreign languages in Vietnam. However, the in-depth studies on cross- language paraphrase identification tasks between English and Vietnamese are still limited. Therefore, in this paper, we propose a method to identify the English–Vietnamese cross-language paraphrase cases, using hybrid feature classes. These classes are calculated by using the fuzzy-based method as well as the siamese recurrent model, and then combined to get the final result with a mathematical formula. The experimental results show that our model achieves 87.4% F-measure accuracy.
- Subjects :
- Control and Optimization
Machine translation
Computer Networks and Communications
Computer science
Vietnamese
Foreign language
0211 other engineering and technologies
02 engineering and technology
Management Science and Operations Research
computer.software_genre
Paraphrase
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Plagiarism detection
021103 operations research
business.industry
Information technology
language.human_language
Feature (linguistics)
Identification (information)
language
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Software
Natural language processing
Information Systems
Subjects
Details
- ISSN :
- 15729397 and 13811231
- Volume :
- 28
- Database :
- OpenAIRE
- Journal :
- Journal of Heuristics
- Accession number :
- edsair.doi...........b786f53cccc81861b46e3ccd19342e5d