Specificity Helps Text Classification

  • Lucas Bouma
  • Maarten de Rijke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3936)


We examine the impact on classification effectiveness of semantic differences in categories. Specifically, we measure broadness and narrowness of categories in terms of their distance to the root of a hierarchically organized thesaurus. Using categories of four different levels degrees of broadness, we show that classifying documents into narrow categories gives better scores than classifying them into broad terms, which we attribute to the fact that more specific categories are associated with terms with a higher discriminatory power.


Kainic Acid Category Label Trypanosoma Cruzi Entamoeba Histolytica Trypanosoma Brucei 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bloehdorn, S., Hotho, A.: Boosting for text classification with semantic features. In: Proceedings of the Workshop on Mining for and from the Semantic Web at the 10th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 70–87 (2004),
  2. 2.
    Dayanik, A., Fradkin, D., Genkin, A., Kantor, P., Madigan, D., Lewis, D., Menkov, V.: Dimacs at the TREC 2004 genomics track. In: The Thirteenth Text Retrieval, Conference, TREC 2004 (2005)Google Scholar
  3. 3.
    Granitzer, M.: Hierarchical Text Classification usingMethods from Machine Learning. Master’s thesis, Graz University of Technology (2003)Google Scholar
  4. 4.
    Hersh, W., Bhuptiraju, R., Ross, L., Johnson, P., Cohen, A., Kraemer, D.: TREC 2004 genomics track overview. In: The Thirteenth Text Retrieval, Conference, TREC 2004 (2005)Google Scholar
  5. 5.
    Joachims, T.: Making large-scale SVM learning practical. In: Scholkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods (1999)Google Scholar
  6. 6.
    MeSH. National library of medicine, medical subject headings, MeSH (2005),
  7. 7.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)CrossRefGoogle Scholar
  8. 8.
    TREC Genomics Track. Trec genomics 2004 ad hoc task documents (2005), URL:
  9. 9.
    Wibowo, W., Williams, H.: On using hierarchies for document classification. In: Proceedings of the Fourth Australasian Document Computing Symposium, Coffs Harbour, Australia (1999)Google Scholar
  10. 10.
    Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques with Java implementations. Morgan Kaufmann Publishers Inc., San Francisco (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lucas Bouma
    • 1
  • Maarten de Rijke
    • 1
  1. 1.ISLAUniversity of AmsterdamAmsterdamThe Netherlands

Personalised recommendations