An unsupervised method for detecting style breaches in a document

Autor:	Seifeddine Mechti, Maryam Elamine, Lamia Hadrich Belguith
Předmět:	Unsupervised algorithm Computer science business.industry Supervised learning k-means clustering Pattern recognition Plagiarism detection Segmentation Artificial intelligence Similarity measure Unsupervised clustering business Style (sociolinguistics)
Zdroj:	Web of Science AICCSA
Popis:	In this paper, we propose an unsupervised method for identifying style breaches in a given document, also known as intrinsic plagiarism detection. In fact, plagiarism is one of the major challenges in various domains. Supervised learning techniques fail to capture the stylistic changes in a text effectively and this is mainly due to the static segmentation of the text. For this reason, we present in this paper our proposed method for intrinsic plagiarism detection, we experimented with the unsupervised algorithm Kmeans and the similarity measure Cosine. Our results are comparable to best systems presented in PAN@CLEF competitive conference.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d9554b6a39b4bd8278344bdefe50056 https://publons.com/wos-op/publon/54706277/ Zobrazit plný text záznamu