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Learning to Classify Text Using a Few Labeled Examples

  • Conference paper
Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 348))

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Abstract

It is well known that supervised text classification methods need to learn from many labeled examples to achieve a high accuracy. However, in a real context, sufficient labeled examples are not always available. In this paper we demonstrate that a way to obtain a high accuracy, when the number of labeled examples is low, is to consider structured features instead of list of weighted words as observed features. The proposed vector of features considers a hierarchical structure, named a mixed Graph of Terms, composed of a directed and an undirected sub-graph of words, that can be automatically constructed from a set of documents through the probabilistic Topic Model.

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Author information

Authors and Affiliations

  1. Department of Electronic Engineering and Computer Engineering, University of Salerno, 84084, Fisciano, Italy

    Francesco Colace, Massimo De Santo & Luca Greco

  2. DISCo (Department of Informatics, Systems and Communication), University of Milan, Bicocca Viale Sarca 336, 20126, Milan, Italy

    Paolo Napoletano

Authors
  1. Francesco Colace
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  2. Massimo De Santo
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  3. Luca Greco
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  4. Paolo Napoletano
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Editor information

Editors and Affiliations

  1. IST - Technical University of Lisbon, Av.Rovisco Pais, 1, 1049-001, Lisbon, Portugal

    Ana Fred

  2. Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands

    Jan L. G. Dietz

  3. Informatics Research Centre, Henley Business School, University of Reading, RG6 6UD, Reading, UK

    Kecheng Liu

  4. INSTICC and IPS, Estefanilha, Setúbal, Portugal

    Joaquim Filipe

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Colace, F., De Santo, M., Greco, L., Napoletano, P. (2013). Learning to Classify Text Using a Few Labeled Examples. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2011. Communications in Computer and Information Science, vol 348. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37186-8_13

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  • DOI: https://doi.org/10.1007/978-3-642-37186-8_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37185-1

  • Online ISBN: 978-3-642-37186-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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