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A Robust Number Parser Based on Conditional Random Fields

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KI 2017: Advances in Artificial Intelligence (KI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10505))

Abstract

When processing information from unstructured sources, numbers have to be parsed in many cases to do useful reasoning on that information. However, since numbers can be expressed in different ways, a robust number parser that can cope with number representations in different shapes is required in those cases. In this paper, we show how to train such a parser based on Conditional Random Fields. As training data, we use pairs of Wikipedia infobox entries and numbers from public knowledge graphs. We show that it is possible to parse numbers at an accuracy of more than 90%.

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Notes

  1. 1.

    http://alias-i.com/lingpipe/.

  2. 2.

    http://alias-i.com/lingpipe/demos/tutorial/crf/read-me.html.

  3. 3.

    This case may occur, e.g., if two sequences differ only in a T and an O.

  4. 4.

    i.e., invoking Double.parseDouble(s) on a string s.

  5. 5.

    https://commoncrawl.org.

  6. 6.

    Runtimes on a commodity Windows laptop.

  7. 7.

    The training of the CRF, however, can take up to several hours, but only needs to be performed once. An executable version with the best pre-trained CRF is available at http://bit.ly/2qRbwDq.

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Author information

Authors and Affiliations

  1. Data and Web Science Group, University of Mannheim, Mannheim, Germany

    Heiko Paulheim

Authors
  1. Heiko Paulheim
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Correspondence to Heiko Paulheim .

Editor information

Editors and Affiliations

  1. Fakultät für Informatik, Technische Universität Dortmund, Dortmund, Germany

    Gabriele Kern-Isberner

  2. FB Informatik, TU Darmstadt, Darmstadt, Hessen, Germany

    Johannes Fürnkranz

  3. FB Informatik, Universität Koblenz, Koblenz, Rheinland-Pfalz, Germany

    Matthias Thimm

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Paulheim, H. (2017). A Robust Number Parser Based on Conditional Random Fields. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds) KI 2017: Advances in Artificial Intelligence. KI 2017. Lecture Notes in Computer Science(), vol 10505. Springer, Cham. https://doi.org/10.1007/978-3-319-67190-1_29

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  • DOI: https://doi.org/10.1007/978-3-319-67190-1_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67189-5

  • Online ISBN: 978-3-319-67190-1

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