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Detecting the origin of text segments efficiently
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International World Wide Web Conference archive
Proceedings of the 18th international conference on World wide web table of contents
Madrid, Spain
SESSION: Data mining/session: text mining table of contents
Pages 61-70  
Year of Publication: 2009
ISBN:978-1-60558-487-4
Authors
Ossama Abdel Hamid  Cairo University, Cairo, Egypt
Behshad Behzadi  Google Inc., Zurich, Switzerland
Stefan Christoph  Google Inc., Zurich, Switzerland
Monika Henzinger  Google Inc. and EPFL Lausanne, Lausanne, Switzerland
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

In the origin detection problem an algorithm is given a set S of documents, ordered by creation time, and a query document D. It needs to output for every consecutive sequence of k alphanumeric terms in D the earliest document in $S$ in which the sequence appeared (if such a document exists). Algorithms for the origin detection problem can, for example, be used to detect the "origin" of text segments in D and thus to detect novel content in D. They can also find the document from which the author of D has copied the most (or show that D is mostly original.) We concentrate on solutions that use only a fixed amount of memory. We propose novel algorithms for this problem and evaluate them together with a large number of previously published algorithms. Our results show that (1) detecting the origin of text segments efficiently can be done with very high accuracy even when the space used is less than 1% of the size of the documents in $S$, (2) the precision degrades smoothly with the amount of available space, (3) various estimation techniques can be used to increase the performance of the algorithms.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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R. A. Baeza-Yates, A. R. P. Jr., and N. Ziviani. Understanding content reuse on the web: Static and dynamic analyses. In WEBKDD, pages 227--246, 2006.
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M. O. Rabin. Fingerprinting by random polynomials. Technidal report, Harvard University, TR-15-81, 1981.
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J. Zobel and Y. Bernstein. The case of the duplicate documents measurement, search, and science. In APWeb, pages 26--39, 2006.

Collaborative Colleagues:
Ossama Abdel Hamid: colleagues
Behshad Behzadi: colleagues
Stefan Christoph: colleagues
Monika Henzinger: colleagues