Cross-language text alignment: A proposed two-level matching scheme for plagiarism detection

摘要

The exponential growth of documents in various languages throughout the web, along with the availability of several editing and translation tools have made the cross-language plagiarism detection a challenging issue. Regarding its high importance, the present study focuses on the task of cross-language text alignment also known as detailed analysis which works on the outputs of the source retrieval step of cross language plagiarism detection systems. The paper proposes a two-level matching approach with the aim of considering both syntactic and semantic information to align plagiarism fragments from the source and suspicious documents, accurately. At the first level, a vector space model which employs a multilingual word embeddings based dictionary and a local weighting technique is used in order to extract a minimal set of highly potential candidate fragment pairs rather than considering all possible pairs of fragments. This step also contains a dynamic expansion technique to cover more candidate pairs aiming at improving the system's recall. It is followed by a more precise algorithm that examines the candidate pairs at the sentence level using a graph-of-words representation of text. As a result, by modelling both the words and their relationships, an acceptable increase in the system's precision which is the goal of the second level is also observed. To identify evidence of plagiarism, i.e. potential cases of unauthorized text reuse, the algorithm tries to find maximum cliques from the match graph of source and suspicious texts. With this two-level investigation, the approach is capable to discriminate true plagiarism cases from the original text. The experimental results on different datasets such as PAN-PC-11, PAN-PC-12, and SemEval-2017 show that the proposed cross-language text alignment approach significantly outperforms the state-of-the-art models and can be fed into an expert system for further improvement of cross-language plagiarism detection. The source codes are publicly available on GitHub1, for the purposes of reproducible research. (c) 2020 Elsevier Ltd. All rights reserved.