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Text Quantification

  • Conference paper
Advances in Information Retrieval (ECIR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8416))

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Abstract

In recent years it has been pointed out that, in a number of applications involving classification, the final goal is not determining which class (or classes) individual unlabelled data items belong to, but determining the prevalence (or “relative frequency”) of each class in the unlabelled data. The latter task has come to be known as quantification [1, 3, 5-10, 15, 18, 19].

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References

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Author information

Authors and Affiliations

  1. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, 56124, Pisa, Italy

    Fabrizio Sebastiani

Authors
  1. Fabrizio Sebastiani
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Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Maarten de Rijke  & Tom Kenter  & 

  2. Centrum Wiskunde en Informatica, Amsterdam, The Netherlands and Delft University of Technology, Delft, The Netherlands

    Arjen P. de Vries

  3. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    ChengXiang Zhai

  4. University of Twente, Twente, The Netheralnds and Erasmus University Rotterdam, Rotterdam, The Netherlands

    Franciska de Jong

  5. SalesPredict, Haifa, Israel

    Kira Radinsky

  6. Microsoft Research, Cambridge, UK

    Katja Hofmann

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© 2014 Springer International Publishing Switzerland

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Sebastiani, F. (2014). Text Quantification. In: de Rijke, M., et al. Advances in Information Retrieval. ECIR 2014. Lecture Notes in Computer Science, vol 8416. Springer, Cham. https://doi.org/10.1007/978-3-319-06028-6_104

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  • DOI: https://doi.org/10.1007/978-3-319-06028-6_104

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06027-9

  • Online ISBN: 978-3-319-06028-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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