When it is not possible to compare the suspicious document to the source document(s) plagiarism has been committed from, the evidence of plagiarism has to be looked for intrinsically in the document itself. In this paper, we introduce a novel languageindependent intrinsic plagiarism detection method which is based on a new text representation that we called n-gram classes. The proposed method was evaluated on three publicly available standard corpora. The obtained results are comparable to the ones obtained by the best state-of-the-art methods.
Problem statement:The way of referring to a place in the geographical space can be formal, based on the spatial coordinates, or informal, which we use in natural language by using toponyms (place names). A toponym can represent several geographical places. This ambiguity made problematic its conversion towards a unique formal representation. Toponym disambiguation in text is the task of assigning a unique location to an ambiguous place name in a given textual context. Approach: Several toponym disambiguation heuristics assumed a geographical proximity between the toponyms of the same context. This proximity can be in terms of spatial distance or in terms of arborsecent relationships, i.e., proximity in the hierarchical tree of the world places. This study presented a new toponym disambiguation heuristic in text based on the quantification of the arborescent proximity between toponyms. This quantification was done by a new measure of geographical correlation that we call the Geographical Density. Results: Our method was compared to the state of the art methods using GeoSemCor corpus and it has outperformed them in term of recall (87.4%) and coverage (99.0%). The results showed that the toponyms of the same context are much closer in terms of arborescent relationships than in terms of spatial relationships. Conclusion: We believe that the quantification of arborescent relationships between toponyms of the same textual context is a good way to improve the recall of TD task. However, all the arborescent relationships' types must be considered and not only the meronymy, which is the relation the most exploited in the existing TD methods.
Bensalem, I.; Rosso, P.; Chikhi, S. (2013)
When a shift in writing style is noticed in a document, doubts arise about its originality. Based on this clue to plagiarism, the intrinsic approach to plagiarism detection identifies the stolen passages by analysing the writing style of the suspicious document without comparing it to textual resources that may serve as sources for the plagiarist. Character n-grams are recognised as a successful approach to modelling text for writing style analysis. Although prior studies have investigated the best practice of using character n-grams in authorship attribution and other problems, there is still a need for such investigations in the context of intrinsic plagiarism detection. Moreover, it has been assumed in previous works that the ways of using character ngrams in authorship attribution remain the same for intrinsic plagiarism detection. In this paper, we study the effect of character n-grams frequency and length on the performance of intrinsic plagiarism detection. Our experiments utilise two state-ofthe-art methods and five large document collections of PAN labs written in English and Arabic. We demonstrate empirically that the low-and the high-frequency ngrams are not equally relevant for intrinsic plagiarism detection, but their performance depends on the way they are exploited. Keywords Intrinsic plagiarism detection . Character n-grams . Stylistic features . Writing style analysisWe are very grateful to the anonymous reviewers for their insightful suggestions and constructive comments that greatly improved the paper.
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