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Combining Pattern-Based and Distributional Similarity for Graph-Based Noun Categorization

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Book cover Natural Language Processing and Information Systems (NLDB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9103))

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Abstract

We examine the combination of pattern-based and distributional similarity for the induction of semantic categories. Pattern-based methods are precise and sparse while distributional methods have a higher recall. Given these particular properties we use the prediction of distributional methods as a back-off to pattern-based similarity. Since our pattern-based approach is embedded into a semi-supervised graph clustering algorithm, we also examine how distributional information is best added to that classifier. Our experiments are carried out on \(5\) different food categorization tasks.

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Notes

  1. 1.

    We remove all food items that contain as a suffix another food item that is also contained in our food vocabulary.

  2. 2.

    That is, in order to establish the label of the sparse compound chocolate-almond cake, one just considers the label of the suffix/head cake. The latter is a more general expression for which a label can be more reliably determined.

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Acknowledgements

This work was supported, in part, by the German Federal Ministry of Education and Research (BMBF) under grant no. 01IC12SO1X and the Information Extraction and Synthesis Lab at the University of Massachusetts. The authors would like to thank Stephanie Köser for annotating the dataset presented in this paper.

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Authors and Affiliations

  1. Spoken Language Systems, Saarland University, Saarbrücken, 66123, Germany

    Michael Wiegand & Dietrich Klakow

  2. School of Computer Science, University of Massachusetts, Amherst, MA, USA

    Benjamin Roth

Authors
  1. Michael Wiegand
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  2. Benjamin Roth
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  3. Dietrich Klakow
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Corresponding author

Correspondence to Michael Wiegand .

Editor information

Editors and Affiliations

  1. Technische Universität Darmstadt, Darmstadt, Germany

    Chris Biemann

  2. Universität Passau, Passau, Germany

    Siegfried Handschuh

  3. Universität Passau, Passau, Germany

    André Freitas

  4. University of Salford, Salford, United Kingdom

    Farid Meziane

  5. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

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Wiegand, M., Roth, B., Klakow, D. (2015). Combining Pattern-Based and Distributional Similarity for Graph-Based Noun Categorization. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science(), vol 9103. Springer, Cham. https://doi.org/10.1007/978-3-319-19581-0_5

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  • DOI: https://doi.org/10.1007/978-3-319-19581-0_5

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