Candidate document retrieval for cross-lingual plagiarism detection using two-level proximity information. Issue 6 (November 2016)

• Record Type: Journal Article
• Authors: Ehsan, Nava; Shakery, Azadeh
• Is Part Of: Information processing & management. Volume 52:Issue 6(2016:Nov.)
• Page Start: 1004
• Page End: 1017
• Publication Date: 2016-11
• Subjects: Candidate document retrieval -- Cross-language plagiarism detection -- Text segmentation -- Proximity-based retrieval; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2016.04.006
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: Proposing a candidate retrieval model for cross-lingual plagiarism detection The method relies on using two levels of proximity information Proposing a topic-based text segmentation method Comparing the method with other cross-lingual plagiarism detection approaches Showing improvements using text segmentation and positional language models Abstract: The rapid growth of documents in different languages, the increased accessibility of electronic documents, and the availability of translation tools have caused cross-lingual plagiarism detection research area to receive increasing attention in recent years. The task of cross-language plagiarism detection entails two main steps: candidate retrieval and assessing pairwise document similarity. In this paper we examine candidate retrieval, where the goal is to find potential source documents of a suspicious text. Our proposed method for cross-language plagiarism detection is a keyword-focused approach. Since plagiarism usually happens in parts of the text, there is a requirement to segment the texts into fragments to detect local similarity. Therefore we propose a topic-based segmentation algorithm to convert the suspicious document to a set of related passages. After that, we use a proximity-based model to retrieve documents with the best matching passages. Experiments show promising results for this important phase of cross-language plagiarism detection.