Using Word Embedding for Cross-Language Plagiarism Detection

Jérémy Ferrero, Laurent Besacier, Didier Schwab, Frédéric Agnès


Abstract
This paper proposes to use distributed representation of words (word embeddings) in cross-language textual similarity detection. The main contributions of this paper are the following: (a) we introduce new cross-language similarity detection methods based on distributed representation of words; (b) we combine the different methods proposed to verify their complementarity and finally obtain an overall F1 score of 89.15% for English-French similarity detection at chunk level (88.5% at sentence level) on a very challenging corpus.
Anthology ID:
E17-2066
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
415–421
Language:
URL:
https://aclanthology.org/E17-2066
DOI:
Bibkey:
Cite (ACL):
Jérémy Ferrero, Laurent Besacier, Didier Schwab, and Frédéric Agnès. 2017. Using Word Embedding for Cross-Language Plagiarism Detection. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 415–421, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Using Word Embedding for Cross-Language Plagiarism Detection (Ferrero et al., EACL 2017)
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PDF:
https://aclanthology.org/E17-2066.pdf
Presentation:
 E17-2066.Presentation.pdf