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An analysis of the relative hardness of Reuters‐21578 subsets

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  • Franca Debole
  • Fabrizio Sebastiani

Abstract

The existence, public availability, and widespread acceptance of a standard benchmark for a given information retrieval (IR) task are beneficial to research on this task, because they allow different researchers to experimentally compare their own systems by comparing the results they have obtained on this benchmark. The Reuters‐21578 test collection, together with its earlier variants, has been such a standard benchmark for the text categorization (TC) task throughout the last 10 years. However, the benefits that this has brought about have somehow been limited by the fact that different researchers have “carved” different subsets out of this collection and tested their systems on one of these subsets only; systems that have been tested on different Reuters‐21578 subsets are thus not readily comparable. In this article, we present a systematic, comparative experimental study of the three subsets of Reuters‐21578 that have been most popular among TC researchers. The results we obtain allow us to determine the relative hardness of these subsets, thus establishing an indirect means for comparing TC systems that have, or will be, tested on these different subsets.

Suggested Citation

  • Franca Debole & Fabrizio Sebastiani, 2005. "An analysis of the relative hardness of Reuters‐21578 subsets," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 56(6), pages 584-596, April.
    • Handle: RePEc:bla:jamist:v:56:y:2005:i:6:p:584-596
    DOI: 10.1002/asi.20147
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    1. Limosani, Michele & Millemaci, Emanuele & Mustica, Paolo, 2023. "An efficient Bayes classifier for word classification: an application on the EU Recovery and Resilience Plans," MPRA Paper 119875, University Library of Munich, Germany.

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