English–Vietnamese cross-language paraphrase identification using hybrid feature classes

Dien Dinh () and Nguyen Le Thanh ()
Additional contact information
Dien Dinh: University of Science, VNU-HCM
Nguyen Le Thanh: University of Science, VNU-HCM

Journal of Heuristics, 2022, vol. 28, issue 2, No 4, 193-209

Abstract: 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.

Keywords: Paraphrase identification; Semantic similarity; Cross-language; BabelNet; Vietnamese (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10732-019-09411-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joheur:v:28:y:2022:i:2:d:10.1007_s10732-019-09411-2

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10732

DOI: 10.1007/s10732-019-09411-2

Access Statistics for this article

Journal of Heuristics is currently edited by Manuel Laguna

More articles in Journal of Heuristics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().