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Argument Mining: A Machine Learning Perspective

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Book cover Theory and Applications of Formal Argumentation (TAFA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9524))

Abstract

Argument mining has recently become a hot topic, attracting the interests of several and diverse research communities, ranging from artificial intelligence, to computational linguistics, natural language processing, social and philosophical sciences. In this paper, we attempt to describe the problems and challenges of argument mining from a machine learning angle. In particular, we advocate that machine learning techniques so far have been under-exploited, and that a more proper standardization of the problem, also with regards to the underlying argument model, could provide a crucial element to develop better systems.

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Notes

  1. 1.

    The First ACL Workshop on Argumentation Mining, http://www.uncg.edu/cmp/ArgMining2014, SICSA Workshop on Argument Mining: Perspectives from Information Extraction, Information Retrieval and Computational Linguistics http://www.arg-tech.org/index.php/sicsa-workshop-on-argument-mining-2014, and the BiCi Workshop on Frontiers and Connections between Argumentation Theory and Natural Language Processing, http://www-sop.inria.fr/members/Serena.Villata/BiCi2014/frontiersARG-NLP.html.

  2. 2.

    More about IBM Debating Technologies at http://researcher.watson.ibm.com/researcher/view_group.php?id=5443.

  3. 3.

    All examples in this paper are taken from the IBM corpus, described in Sect. 4.

  4. 4.

    http://corpora.aifdb.org.

  5. 5.

    http://www.debatepedia.com.

  6. 6.

    http://www.procon.org.

  7. 7.

    https://www.research.ibm.com/haifa/dept/vst/mlta_data.shtml.

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Authors and Affiliations

  1. DISI, Università Degli Studi di Bologna, Bologna, Italy

    Marco Lippi & Paolo Torroni

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  1. Marco Lippi
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  2. Paolo Torroni
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Correspondence to Marco Lippi .

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Editors and Affiliations

  1. King's College London, London, United Kingdom

    Elizabeth Black

  2. Strand Building;Strand Campus, King's College London, London, United Kingdom

    Sanjay Modgil

  3. Department of Computing Science, University of Aberdeen, Aberdeen, United Kingdom

    Nir Oren

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Lippi, M., Torroni, P. (2015). Argument Mining: A Machine Learning Perspective. In: Black, E., Modgil, S., Oren, N. (eds) Theory and Applications of Formal Argumentation. TAFA 2015. Lecture Notes in Computer Science(), vol 9524. Springer, Cham. https://doi.org/10.1007/978-3-319-28460-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-28460-6_10

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