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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/E17-2066.pdf