Abstract
We present a novel general method for discovering similar passages within large text documents based on adapting and extending the well-known Smith-Waterman dynamic programming local sequence alignment algorithm. We extend that algorithm for large document analysis by defining: (a) a recursive procedure for discovering multiple non-overlapping aligned passages within a given document pair; (b) a matrix splicing method for processing long texts; (c) a chaining method for combining sequence strands; and (d) an inexact similarity measure for determining token matches. We show that an implementation of this method is computationally efficient and produces very high precision with good recall for several types of order-based plagiarism and that it achieves higher overall performance than the best reported methods against the PAN 2013 text alignment test corpus.
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Glinos, D. (2014). Discovering Similar Passages within Large Text Documents. In: Kanoulas, E., et al. Information Access Evaluation. Multilinguality, Multimodality, and Interaction. CLEF 2014. Lecture Notes in Computer Science, vol 8685. Springer, Cham. https://doi.org/10.1007/978-3-319-11382-1_10
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DOI: https://doi.org/10.1007/978-3-319-11382-1_10
Publisher Name: Springer, Cham
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