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Classification-Based Referring Expression Generation

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Computational Linguistics and Intelligent Text Processing (CICLing 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8403))

Abstract

This paper presents a study in the field of Natural Language Generation (NLG), focusing on the computational task of referring expression generation (REG). We describe a standard REG implementation based on the well-known Dale & Reiter Incremental algorithm, and a classification-based approach that combines the output of several support vector machines (SVMs) to generate definite descriptions from two publicly available corpora. Preliminary results suggest that the SVM approach generally outperforms incremental generation, which paves the way to further research on machine learning methods applied to the task.

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Author information

Authors and Affiliations

  1. School of Arts, Sciences and Humanities, University of São Paulo (USP / EACH), Av. Arlindo Bettio, 1000, São Paulo, Brazil

    Thiago Castro Ferreira & Ivandré Paraboni

Authors
  1. Thiago Castro Ferreira
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  2. Ivandré Paraboni
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Editor information

Editors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, Av. Juan Dios Bátiz, Col. Nueva Industrial Vallejo, 07738, Mexico D.F., Mexico

    Alexander Gelbukh

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Ferreira, T.C., Paraboni, I. (2014). Classification-Based Referring Expression Generation. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54906-9_39

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  • DOI: https://doi.org/10.1007/978-3-642-54906-9_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54905-2

  • Online ISBN: 978-3-642-54906-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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