Abstract
It is one of key problems in Text Mining to find document features. The string matching model and global word frequency model are two common models. But the former can hardly resist rewording noise, whereas the latter cannot find document details. We present Common Semantic Sequence Model (CSSM) and apply it to Document Copy Detection. CSSM combines the ideas of 2 models above, and it makes a trade-off between a document global features and local features. CSSM calculates the common words proportion between 2 documents semantic sequences to make a plagiarism score. A semantic sequence is indeed a continual word sequence after the low-density words are omitted. With the collection of 2 documents semantic sequences, we can detect plagiarism in a fine granularity. We test CSSM with several common copy types. The result shows that CSSM is excellent for detecting non-rewording plagiarism and valid even if documents are reworded to some extent.
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References
Bao, J.P., et al.: Document copy detection based on kernel method. In: Proceedings of the International Conference on Natural Language Processing and Knowledge Engineering, October 26-29, pp. 250–256 (2003)
Broder, A.Z., Glassman, S.C., Manasse, M.S.: Syntactic Clustering of the Web. In: Sixth International Web Conference, Santa Clara, California USA, April 7-11 (1997)
Denning, P.J.: Editorial: Plagiarism in the web. Communications of the ACM 38(12) (1995)
Heintze, N.: Scalable Document Fingerprinting. In: Proceedings of the Second USENIX Workshop on Electronic Commerce, Oakland, California, November 18-21 (1996)
Monostori, K., Zaslavsky, A., Schmidt, H.: MatchDetectReveal: Finding Overlapping and Similar Digital Documents. In: Proceedings of Information Resources Management Association International Conference (IRMA2000), Anchorage, Alaska, USA, May 21-24 (2000)
Salton, G., Wong, A., Yang, C.: A vector space model for automatic indexing. Communications of the ACM 18(11), 613–620 (1975)
Shivakumar, N., Garcia-Molina, H.: SCAM: A copy detection mechanism for digital documents. In: Proceedings of 2nd International Conference in Theory and Practice of Digital Libraries (DL 1995), Austin, Texas (June 1995)
Si, A., Leong, H.V., Lau, R.W.H.: CHECK: A Document Plagiarism Detection System. In: Proceedings of ACM Symposium for Applied Computing, February 1997, pp. 70–77 (1997)
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© 2004 Springer-Verlag Berlin Heidelberg
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Bao, JP., Shen, JY., Liu, XD., Liu, HY., Zhang, XD. (2004). Finding Plagiarism Based on Common Semantic Sequence Model. In: Li, Q., Wang, G., Feng, L. (eds) Advances in Web-Age Information Management. WAIM 2004. Lecture Notes in Computer Science, vol 3129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27772-9_66
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DOI: https://doi.org/10.1007/978-3-540-27772-9_66
Publisher Name: Springer, Berlin, Heidelberg
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