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Natural Language Argumentation for Text Exploration

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Agents and Artificial Intelligence (ICAART 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10162))

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Abstract

Argumentation mining aims at automatically extracting natural language arguments from textual documents. In the last years, it has become a hot topic due to its potential in processing information originating from the Web in innovative ways. In this paper, we propose to apply the argument mining pipeline to the text exploration task. First, starting from the arguments put forward in online debates, we introduce bipolar entailment graphs to predict the relation among the textual arguments, i.e., entailment or non entailment relation. Second, we exploit the well know formalism called abstract dialectical frameworks to define acceptance conditions answering the needs of the text exploration task. The evaluation of the proposed approach shows its feasibility.

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Notes

  1. 1.

    As discussed also in the keynote talk of the Joint Symposium on Semantic Processing (http://jssp2013.fbk.eu/).

  2. 2.

    https://www.cs.cornell.edu/home/cardie/naacl-2nd-arg-mining/.

  3. 3.

    In the two-way classification task, contradiction and unknown relations are collapsed into a unique relation, i.e. non entailment.

  4. 4.

    [13] provides an overview of the recent advances in TE.

  5. 5.

    The Recognizing Textual Entailment (RTE) data are not suitable for our goal, since the pairs are not interconnected (i.e. they cannot be transformed into argumentation graphs).

  6. 6.

    http://idebate.org/.

  7. 7.

    http://bit.ly/RTE-challenge.

  8. 8.

    http://hltfbk.github.io/Excitement-Open-Platform/.

  9. 9.

    The F-measure is a measure of accuracy. It considers both the precision and the recall of the test to compute the score.

  10. 10.

    Complexity results for ADFs have been studied by [6].

  11. 11.

    http://www.dbai.tuwien.ac.at/research/project/adf/.

  12. 12.

    We refer the interested reader to the results of the First International Competition on Computational Models of Argumentation [33].

  13. 13.

    http://bit.ly/DebatepediaExtended.

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Author information

Authors and Affiliations

  1. University of Nice Sophia Antipolis, Sophia Antipolis, France

    Elena Cabrio

  2. CNRS - I3S Laboratory, Sophia Antipolis, France

    Serena Villata

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  1. Elena Cabrio
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  2. Serena Villata
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Correspondence to Serena Villata .

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Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

  2. Polytechnic Institute of Setúbal/INSTICC, Setúbal, Portugal

    Joaquim Filipe

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Cabrio, E., Villata, S. (2017). Natural Language Argumentation for Text Exploration. In: van den Herik, J., Filipe, J. (eds) Agents and Artificial Intelligence. ICAART 2016. Lecture Notes in Computer Science(), vol 10162. Springer, Cham. https://doi.org/10.1007/978-3-319-53354-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-53354-4_8

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