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Text Reuse Detection Using a Composition of Text Similarity Measures

Bär, Daniel and Zesch, Torsten and Gurevych, Iryna (2012):
Text Reuse Detection Using a Composition of Text Similarity Measures.
In: Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012), Mumbai, India, [Online-Edition: http://aclweb.org/anthology/C12-1011],
[Conference or Workshop Item]

Abstract

Detecting text reuse is a fundamental requirement for a variety of tasks and applications, ranging from journalistic text reuse to plagiarism detection. Text reuse is traditionally detected by computing similarity between a source text and a possibly reused text. However, existing text similarity measures exhibit a major limitation: They compute similarity only on features which can be derived from the content of the given texts, thereby inherently implying that any other text characteristics are negligible. In this paper, we overcome this traditional limitation and compute similarity along three characteristic dimensions inherent to texts: content, structure, and style. We explore and discuss possible combinations of measures along these dimensions, and our results demonstrate that the composition consistently outperforms previous approaches on three standard evaluation datasets, and that text reuse detection greatly benefits from incorporating a diverse feature set that reflects a wide variety of text characteristics.

Item Type: Conference or Workshop Item
Erschienen: 2012
Creators: Bär, Daniel and Zesch, Torsten and Gurevych, Iryna
Title: Text Reuse Detection Using a Composition of Text Similarity Measures
Language: English
Abstract:

Detecting text reuse is a fundamental requirement for a variety of tasks and applications, ranging from journalistic text reuse to plagiarism detection. Text reuse is traditionally detected by computing similarity between a source text and a possibly reused text. However, existing text similarity measures exhibit a major limitation: They compute similarity only on features which can be derived from the content of the given texts, thereby inherently implying that any other text characteristics are negligible. In this paper, we overcome this traditional limitation and compute similarity along three characteristic dimensions inherent to texts: content, structure, and style. We explore and discuss possible combinations of measures along these dimensions, and our results demonstrate that the composition consistently outperforms previous approaches on three standard evaluation datasets, and that text reuse detection greatly benefits from incorporating a diverse feature set that reflects a wide variety of text characteristics.

Title of Book: Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012)
Uncontrolled Keywords: UKP_a_NLP4Wikis;UKP_p_WIKULU;reviewed;UKP_s_DKPro_Similarity;UKP_p_ItForensics;UKP_a_TexMinAn
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Event Location: Mumbai, India
Date Deposited: 31 Dec 2016 14:29
Official URL: http://aclweb.org/anthology/C12-1011
Identification Number: TUD-CS-2012-0218
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