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Semi-automatic Document Classification: Exploiting Document Difficulty

  • Miguel Martinez-Alvarez
  • Sirvan Yahyaei
  • Thomas Roelleke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7224)

Abstract

There are circumstances where classification is required only if a certain condition, such a specific level of quality, is met. This paper investigates a semi-automatic solution where only the predictions for the documents which are more likely to be correctly classified would be considered. This method provides high-quality automatic classification for large subsets of the collection and employs human expertise for the “most complicated” decisions. This research presents different approaches to measure document difficulty and it discusses the benefits of applying it for semi-automatic classification. In addition, experiments are carried out to show the results achieved for different subsets of the collection. Experiments prove that it is possible to improve quality significantly with large subsets (i.e. 13% micro-f 1 increase with 70% of documents) of two different collections. Furthermore, it shows how it provides a flexible mechanism to apply automatic classification to specific subsets while specific constrains are met.

Keywords

Text Categorization Human Expertise Large Subset Automatic Classification Test Collection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Carmel, D., Yom-Tov, E., Darlow, A., Pelleg, D.: What makes a query difficult? In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2006, pp. 390–397. ACM, New York (2006)CrossRefGoogle Scholar
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    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34, 1–47 (2002)MathSciNetCrossRefGoogle Scholar
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    Yang, Y.: A study on thresholding strategies for text categorization. In: Proceedings of SIGIR 2001, 24th ACM International Conference on Research and Development in Information Retrieval, pp. 137–145. ACM Press (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Miguel Martinez-Alvarez
    • 1
    • 2
  • Sirvan Yahyaei
    • 1
  • Thomas Roelleke
    • 1
  1. 1.Queen Mary, University of LondonUK
  2. 2.Globe Business Publishing Ltd.UK

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