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Clustering short texts using wikipedia

Published:23 July 2007Publication History

ABSTRACT

Subscribers to the popular news or blog feeds (RSS/Atom) often face the problem of information overload as these feed sources usually deliver large number of items periodically. One solution to this problem could be clustering similar items in the feed reader to make the information more manageable for a user. Clustering items at the feed reader end is a challenging task as usually only a small part of the actual article is received through the feed. In this paper, we propose a method of improving the accuracy of clustering short texts by enriching their representation with additional features from Wikipedia. Empirical results indicate that this enriched representation of text items can substantially improve the clustering accuracy when compared to the conventional bag of words representation.

References

  1. E. Gabrilovich. Feature Generation for Textual Information Retrieval Using World Knowledge. PhD Thesis, Department of Computer Science, Technion -- Israel Institute of Technology, Haifa, Israel, 2006.Google ScholarGoogle Scholar
  2. A. Hotho, S. Staab, and G. Stumme. Ontologies Improve Text Document Clustering, In the Proc of the Third IEEE International Conference on Data Mining (ICDM'03), Melbourne, Florida, USA, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. G. Salton, editor. Automatic text processing. Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Clustering short texts using wikipedia

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      • Published in

        cover image ACM Conferences
        SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
        July 2007
        946 pages
        ISBN:9781595935977
        DOI:10.1145/1277741

        Copyright © 2007 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 23 July 2007

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        Overall Acceptance Rate792of3,983submissions,20%

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