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Selecting Interesting Articles Using Their Similarity Based Only on Positive Examples

  • Conference paper
Computational Linguistics and Intelligent Text Processing (CICLing 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3406))

Abstract

The task of automated searching for interesting text documents frequently suffers from a very poor balance among documents representing both positive and negative examples or from one completely missing class. This paper suggests the ranking approach based on the k-NN algorithm adapted for determining the similarity degree of new documents just to the representative positive collection. From the viewpoint of the precision-recall relation, a user can decide in advance how many and how similar articles should be released through a filter.

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References

  1. Hroza, J., Žižka, J., Bourek, A.: Filtering Very Similar Text Documents: A Case Study. In: Gelbukh, A. (ed.) CICLing 2004. LNCS, vol. 2945, pp. 511–520. Springer, Heidelberg (2004)

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Author information

Authors and Affiliations

  1. Faculty of Informatics, Department of Information Technologies, Masaryk University, Botanická 68a, 602 00, Brno, Czech Republic

    Jiří Hroza & Jan Žižka

Authors
  1. Jiří Hroza
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  2. Jan Žižka
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Editor information

Editors and Affiliations

  1. National Polytechnic Institute, Center for Computing Research, 07738, Mexico City, México

    Alexander Gelbukh

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© 2005 Springer-Verlag Berlin Heidelberg

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Hroza, J., Žižka, J. (2005). Selecting Interesting Articles Using Their Similarity Based Only on Positive Examples. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2005. Lecture Notes in Computer Science, vol 3406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30586-6_65

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  • DOI: https://doi.org/10.1007/978-3-540-30586-6_65

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24523-0

  • Online ISBN: 978-3-540-30586-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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