UsingWord Embedding for Cross-Language Plagiarism Detection
This addresses plagiarism detection across languages, but it is incremental as it applies word embeddings to an existing problem.
The paper tackled cross-language plagiarism detection by proposing new methods based on word embeddings, achieving an overall F1 score of 89.15% for English-French similarity detection at chunk level.
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.