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General Representation Model for Text Similarity

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Future and Emerging Trends in Language Technology. Machine Learning and Big Data (FETLT 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10341))

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Abstract

Text similarity is a central issue in multiple information access tasks. General speaking, most of existing similarity models focus on a particular kind of text features such as words, n-grams, or linguistic features or distributional semantics units. In this paper, we introducea general theoretical model for integrating multiple sources in the text feature representation called Feature Projection Information model. The proposed model allows us to integrate traditional features such as words with other sources such as the output of classifiers over different categories or distributional semantics information. The theoretical analysis shows that traditional approaches can be seen as particularizations of the model. Our first empirical results support the idea that additional features in the representation step outperform the predictive power of similarity measures.

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Notes

  1. 1.

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Acknowledgments

This research was supported by the Spanish Ministry of Science and Innovation (Vox-Populi project TIN2013-47090C3-1-P).

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Authors and Affiliations

  1. National Distance University, UNED, C/Juan del Rosal 16, Madrid, Spain

    Fernando Giner & Enrique Amigó

Authors
  1. Fernando Giner
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  2. Enrique Amigó
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Corresponding authors

Correspondence to Fernando Giner or Enrique Amigó .

Editor information

Editors and Affiliations

  1. University of Seville , Sevilla, Spain

    José F Quesada

  2. University of Seville , Seville, Spain

    Francisco-Jesús Martín Mateos

  3. University of Seville , Seville, Spain

    Teresa López Soto

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Giner, F., Amigó, E. (2017). General Representation Model for Text Similarity. In: Quesada, J., Martín Mateos , FJ., López Soto, T. (eds) Future and Emerging Trends in Language Technology. Machine Learning and Big Data. FETLT 2016. Lecture Notes in Computer Science(), vol 10341. Springer, Cham. https://doi.org/10.1007/978-3-319-69365-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-69365-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69364-4

  • Online ISBN: 978-3-319-69365-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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