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Towards a Relation-Based Argument Extraction Model for Argumentation Mining

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Statistical Language and Speech Processing (SLSP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10583))

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Abstract

Argumentation mining aims to detect and identify the argumentative content expressed in text. In this paper we present a relation-based approach that aims to capture the relation of inference between the premise and conclusion. We follow a supervised machine learning approach and explore features at different levels of abstraction. Then, we apply this system for the task of argumentative sentence detection and compare the performance of the system with a competitive baseline approach. The corpus used in our experiments was annotated with arguments from textual resources written in Portuguese, namely opinion articles. The proposed system outperforms the baseline system, achieving 0.75 of f1-score on the test set.

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Notes

  1. 1.

    http://corpora.aifdb.org/ArgMine.

  2. 2.

    http://polyglot.readthedocs.io/en/latest/index.html.

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Acknowledgments

The first author is partially supported by a doctoral grant from Doctoral Program in Informatics Engineering (ProDEI) from the Faculty of Engineering of the University of Porto (FEUP).

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Authors and Affiliations

  1. LIACC/DEI, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Gil Rocha & Henrique Lopes Cardoso

Authors
  1. Gil Rocha
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  2. Henrique Lopes Cardoso
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Correspondence to Gil Rocha .

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Editors and Affiliations

  1. University of Le Mans, Le Mans, France

    Nathalie Camelin

  2. University of Le Mans, Le Mans, France

    Yannick Estève

  3. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

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Rocha, G., Lopes Cardoso, H. (2017). Towards a Relation-Based Argument Extraction Model for Argumentation Mining. In: Camelin, N., Estève, Y., Martín-Vide, C. (eds) Statistical Language and Speech Processing. SLSP 2017. Lecture Notes in Computer Science(), vol 10583. Springer, Cham. https://doi.org/10.1007/978-3-319-68456-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-68456-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68455-0

  • Online ISBN: 978-3-319-68456-7

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