Using Word Embedding for Cross-Language Plagiarism Detection

Ferrero, Jérémy; Besacier, Laurent; Schwab, Didier; Agnès, Frédéric

doi:10.18653/v1/e17-2066

Cited by 35 publications

(25 citation statements)

References 12 publications

(11 reference statements)

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“…The research on plagiarism detection has yielded many approaches that employ lexical [4,18], syntactical [33,44], semantic [41,27], or cross-lingual text analysis [12,14]. These approaches reliably detect copied or moderately altered plagiarism.…”

Section: Related Workmentioning

confidence: 99%

Detecting Machine-Obfuscated Plagiarism

Foltýnek

Ruas

Scharpf

et al. 2020

Sustainable Digital Communities

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Research on academic integrity has identified online paraphrasing tools as a severe threat to the effectiveness of plagiarism detection systems. To enable the automated identification of machineparaphrased text, we make three contributions. First, we evaluate the effectiveness of six prominent word embedding models in combination with five classifiers for distinguishing human-written from machine-paraphrased text. The best performing classification approach achieves an accuracy of 99.0% for documents and 83.4% for paragraphs. Second, we show that the best approach outperforms human experts and established plagiarism detection systems for these classification tasks. Third, we provide a Web application that uses the best performing classification approach to indicate whether a text underwent machine-paraphrasing. The data and code of our study are openly available.

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Section: Related Workmentioning

confidence: 99%

Detecting Machine-Obfuscated Plagiarism

Foltýnek

Ruas

Scharpf

et al. 2020

Sustainable Digital Communities

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“…Word embeddings are becoming an effective way to represent words by relatively low-dimensional vectors, where semantic relatedness is easily measured by the cosine similarity of two word vectors. Ferrero et al [8] introduce a syntax weighting in distributed representations of sentences, and prove its usefulness for textual similarity detection. But in their approach, they only utilized one large dataset to build word vectors.…”

Section: Related Workmentioning

confidence: 99%

Detection of Mergeable Wikipedia Articles Utilizing Multiple Similarity Measures

Wang

Iwaihara

2020

Journal of Information Processing

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Wikipedia is the largest online encyclopedia, in which articles are edited by different volunteers with different thoughts and styles. Sometimes two or more articles' titles are different but the themes of these articles are exactly the same or strongly similar. Administrators and editors are supposed to detect such article pairs and determine whether they should be merged together. We call an article pair is mergeable if it is discussed for possible merge, and a merged article pair is such that the pair is actually merged. In this paper, we propose a method to automatically determine whether an article pair is mergeable or merged. According to Wikipedia Guidelines for article merge, in the duplicate case, the article pairs are covering exactly the same contents. In the overlap case, the article pairs are covering related subjects that have a significant overlap. The content of an overlapped part is similar but the words in the pair can be extensively different, so methods that exploit semantic relatedness are necessary. We consider various textual similarities and semantic relatedness. For integrating word embeddings on the target dataset and the global large corpus, we propose linear and non-linear combinations of multiple embedding results and rebuilding word vectors for evaluating semantic relatedness. We clarify the differences between our method and previous researches for combining multiple word embeddings. We also deal with overlap cases by computing Jaccard similarity between article pairs. We combine Jaccard similarity, common-link article count and word embedding-based relatedness together, to predict whether the article pair should be merged. We explore the relationship between segment-level (paragraph-level) similarity and mergeable/merged article pairs, then propose Multimodal Similarity-Based Merge Prediction (MSBMP) which combines the proposed new features by Random Forest, to predict mergeable/merged article pairs. Our evaluations are performed on real mergeable and merged article pairs. Remarkable superiorities of MSBMP are shown, with apparent improvement from baselines of WikiSearch, TFIDF and word embeddings.

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“…CL-WES (Ferrero et al, 2017) consists in a cosine similarity on distributed representations of sentences, which are obtained by the weighted sum of each word vector in a sentence. As in previous section, each word vector is syntactically and frequentially weighted.…”

Section: Cross-language Word Embedding-based Similaritymentioning

confidence: 99%

CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity

Ferrero¹,

Besacier²,

Schwab³

et al. 2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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We present our submitted systems for Semantic Textual Similarity (STS) Track 4 at SemEval-2017. Given a pair of SpanishEnglish sentences, each system must estimate their semantic similarity by a score between 0 and 5. In our submission, we use syntax-based, dictionary-based, context-based, and MT-based methods. We also combine these methods in unsupervised and supervised way. Our best run ranked 1 st on track 4a with a correlation of 83.02% with human annotations.

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Using Word Embedding for Cross-Language Plagiarism Detection

Cited by 35 publications

References 12 publications

Detecting Machine-Obfuscated Plagiarism

Detecting Machine-Obfuscated Plagiarism

Detection of Mergeable Wikipedia Articles Utilizing Multiple Similarity Measures

CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity

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