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A Computational Model of Text Reuse in Ancient Literary Texts.pdf

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A Computational Model of Text Reuse in Ancient Literary Texts

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 472–479, Prague, Czech Republic, June 2007. c?2007 Association for Computational Linguistics A Computational Model of Text Reuse in Ancient Literary Texts John Lee Spoken Language Systems MIT Computer Science and Artificial Intelligence Laboratory Cambridge, MA 02139, USA jsylee@ Abstract We propose a computational model of text reuse tailored for ancient literary texts, avail- able to us often only in small and noisy sam- ples. The model takes into account source alternation patterns, so as to be able to align even sentences with low surface similarity. We demonstrate its ability to characterize text reuse in the Greek New Testament. 1 Introduction Text reuse is the transformation of a source text into a target text in order to serve a different purpose. Past research has addressed a variety of text-reuse appli- cations, including: journalists turning a news agency text into a newspaper story (Clough et al., 2002); ed- itors adapting an encyclopedia entry to an abridged version (Barzilay and Elhadad, 2003); and plagia- rizers disguising their sources by removing surface similarities (Uzuner et al., 2005). A common assumption in the recovery of text reuse is the conservation of some degree of lexi- cal similarity from the source sentence to the de- rived sentence. A simple approach, then, is to de- fine a lexical similarity measure and estimate a score threshold; given a sentence in the target text, if the highest-scoring sentence in the source text is above the threshold, then the former is considered to be de- rived from the latter. Obviously, the effectiveness of this basic approach depends on the degree of lexical similarity: source sentences that are quoted verba- tim are easier to identify than those that have been transformed by a skillful plagiarizer. The crux of the question, therefore, is how to identify source sentences despite their lack of sur- face similarity