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Towards a Relation-Based Argument Extraction Model for Argumentation Mining

  • Gil RochaEmail author
  • Henrique Lopes Cardoso
Conference paper
  • 608 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10583)

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.

Keywords

Information extraction Argumentation mining Machine learning Natural language processing 

Notes

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.LIACC/DEI, Faculdade de EngenhariaUniversidade do PortoPortoPortugal

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