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International Conference on Text, Speech, and Dialogue

TSD 2017: Text, Speech, and Dialogue pp 362–370Cite as

Temporal Feature Space for Text Classification

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10415))

Abstract

In supervised learning algorithms for text classification the text content is usually represented using the frequencies of the words it contains, ignoring their semantic and their relationships. Words within temporal expressions such as “ today ” or “ last February ” are particularly affected by this simplification: the same expression can have a different semantic in documents with different timestamps, while different expressions could refer to the same time. After extracting temporal expressions in documents, we model a set of temporal features derived from the time mentioned in the document, showing the relation between these features and the belonging category. We test our temporal approach on a subset of the New York Times corpus showing a significant improvement over the text-only baseline.

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Notes

  1. 1.

    Dataset with precomputed features available at https://smartdata.cs.unibo.it/data/TFTC/.

  2. 2.

    NYT Corpus available at https://catalog.ldc.upenn.edu/LDC2008T19.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Bologna, Mura Anteo Zamboni 7, Bologna, Italy

    Stefano Giovanni Rizzo & Danilo Montesi

Authors
  1. Stefano Giovanni Rizzo
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  2. Danilo Montesi
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Corresponding author

Correspondence to Stefano Giovanni Rizzo .

Editor information

Editors and Affiliations

  1. University of West Bohemia, Pilsen, Czech Republic

    Kamil Ekštein

  2. University of West Bohemia, Pilsen, Czech Republic

    Václav Matoušek

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Rizzo, S.G., Montesi, D. (2017). Temporal Feature Space for Text Classification. In: Ekštein, K., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2017. Lecture Notes in Computer Science(), vol 10415. Springer, Cham. https://doi.org/10.1007/978-3-319-64206-2_41

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  • DOI: https://doi.org/10.1007/978-3-319-64206-2_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64205-5

  • Online ISBN: 978-3-319-64206-2

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