skip to main content
research-article

A Universal Model for Discourse-Level Argumentation Analysis

Published:12 June 2017Publication History
Skip Abstract Section

Abstract

The argumentative structure of texts is increasingly exploited for analysis tasks, for example, for stance classification or the assessment of argumentation quality. Most existing approaches, however, model only the local structure of single arguments. This article considers the question of how to capture the global discourse-level structure of a text for argumentation-related analyses. In particular, we propose to model the global structure as a flow of “task-related rhetorical moves,” such as discourse functions or aspect-based sentiment. By comparing the flow of a text to a set of common flow patterns, we map the text into the feature space of global structures, thus capturing its discourse-level argumentation. We show how to identify different types of flow patterns, and we provide evidence that they generalize well across different domains of texts. In our evaluation for two analysis tasks, the classification of review sentiment and the scoring of essay organization, the features derived from flow patterns prove both effective and more robust than strong baselines. We conclude with a discussion of the universality of modeling flow for discourse-level argumentation analysis.

References

  1. Khalid Al-Khatib, Henning Wachsmuth, Matthias Hagen, Jonas Köhler, and Benno Stein. 2016. Cross-domain mining of argumentative text through distant supervision. In Proc. of the 15th NAACL: HLT. 1395--1404.Google ScholarGoogle ScholarCross RefCross Ref
  2. Philippe Besnard and Anthony Hunter. 2008. Elements of Argumentation. The MIT Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. John Blitzer, Ryan McDonald, and Fernando Pereira. 2006. Domain adaptation with structural correspondence learning. In Proc. of the 2006 EMNLP. 120--128. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Leo Breiman. 2001. Random forests. Mach. Learn. 45, 1 (2001), 5--32. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Stefanie Brüninghaus and Kevin D. Ashley. 2003. Predicting outcomes of case based legal arguments. In Proceedings of the 9th International Conference on Artificial Intelligence and Law. 233--242. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Elena Cabrio and Serena Villata. 2012. Combining textual entailment and argumentation theory for supporting online debates interactions. In Proc. of the 50th ACL: Short Papers. 208--212. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Sung-Hyuk Cha. 2007. Comprehensive survey on distance/similarity measures between probability density functions. Int. J. Math. Models Methods Appl. Sci. 1, 4 (2007), 300--307.Google ScholarGoogle Scholar
  8. Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2 (2011), 27:1--27:27. Issue 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Hal Daumé, III and Daniel Marcu. 2006. Domain adaptation for statistical classifiers. J. Artif. Intell. Res. 26, 1 (2006), 101--126. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Semire Dikli. 2006. An overview of automated scoring of essays. J. Technol. Learn. Assess. 5, 1 (2006).Google ScholarGoogle Scholar
  11. Adam Robert Faulkner. 2014. Automated Classification of Argument Stance in Student Essays. Dissertation. City University of New York.Google ScholarGoogle Scholar
  12. Vanessa Wei Feng, Ziheng Lin, and Graeme Hirst. 2014. The impact of deep hierarchical discourse structures in the evaluation of text coherence. In Proc. of the 25th COLING: Technical Papers. 940--949.Google ScholarGoogle Scholar
  13. Vanessa Wei Feng and Graeme Hirst. 2011. Classifying arguments by scheme. In Proc. of the 49th ACL: HLT - Volume 1. 987--996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. James B. Freeman. 2011. Argument Structure: Representation and Theory. Springer.Google ScholarGoogle ScholarCross RefCross Ref
  15. Evgeniy Gabrilovich and Shaul Markovitch. 2007. Computing semantic relatedness using wikipedia-based explicit semantic analysis. In Proc. of the 20th IJCAI. 1606--1611. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Sylviane Granger, Estelle Dagneaux, and and Magali Paquot Fanny Meunier. 2009. International Corpus of Learner English (Version 2). (2009).Google ScholarGoogle Scholar
  17. Ivan Habernal and Iryna Gurevych. 2015. Exploiting debate portals for semi-supervised argumentation mining in user-generated web discourse. In Proc. of the 2015 EMNLP. 2127--2137.Google ScholarGoogle ScholarCross RefCross Ref
  18. Johannes Kiesel, Khalid Al-Khatib, Matthias Hagen, and Benno Stein. 2015. A shared task on argumentation mining in newspaper editorials. In Proc. of the 2nd Workshop on Argumentation Mining. 35--38.Google ScholarGoogle ScholarCross RefCross Ref
  19. William C. Mann and Sandra A. Thompson. 1988. Rhetorical structure theory: Toward a functional theory of text organization. Text 8, 3 (1988), 243--281.Google ScholarGoogle ScholarCross RefCross Ref
  20. Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Yi Mao and Guy Lebanon. 2007. Isotonic conditional random fields and local sentiment flow. Adv. Neural Inf. Process. Syst. 19 (2007), 961--968. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Rohith Menon and Yejin Choi. 2011. Domain independent authorship attribution without domain adaptation. In Proc. of the RANLP 2011. 309--315.Google ScholarGoogle Scholar
  23. Raquel Mochales and Marie-Francine Moens. 2011. Argumentation mining. AI Law 19, 1 (2011), 1--22. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Nathan Ong, Diane Litman, and Alexandra Brusilovsky. 2014. Ontology-based argument mining and automatic essay scoring. In Proc. of the 1st Workshop on Argumentation Mining. 24--28.Google ScholarGoogle ScholarCross RefCross Ref
  25. Bo Pang and Lillian Lee. 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proc. of the 43rd ACL. 115--124. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Joonsuk Park and Claire Cardie. 2014. Identifying appropriate support for propositions in online user comments. In Proc. of the First Workshop on Argumentation Mining. 29--38.Google ScholarGoogle ScholarCross RefCross Ref
  27. Andreas Peldszus and Manfred Stede. 2015. Joint prediction in MST-style discourse parsing for argumentation mining. In Proc. of the 2015 EMNLP. 938--948.Google ScholarGoogle ScholarCross RefCross Ref
  28. Isaac Persing, Alan Davis, and Vincent Ng. 2010. Modeling organization in student essays. In Proc. of the 2010 EMNLP. 229--239. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Isaac Persing and Vincent Ng. 2015. Modeling argument strength in student essays. In Proc. of the 53rd ACL and the 7th IJCNLP. 543--552.Google ScholarGoogle ScholarCross RefCross Ref
  30. Ruty Rinott, Lena Dankin, Carlos Alzate Perez, Mitesh M. Khapra, Ehud Aharoni, and Noam Slonim. 2015. Show me your evidence -- An automatic method for context dependent evidence detection. In Proc. of the 2015 EMNLP. 440--450.Google ScholarGoogle ScholarCross RefCross Ref
  31. Parinaz Sobhani, Diana Inkpen, and Stan Matwin. 2015. From argumentation mining to stance classification. In Proc. of the 2nd Workshop on Argumentation Mining. 67--77.Google ScholarGoogle ScholarCross RefCross Ref
  32. Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proc. of the 2013 EMNLP. 1631--1642.Google ScholarGoogle Scholar
  33. Christian Stab and Iryna Gurevych. 2014. Identifying argumentative discourse structures in persuasive essays. In Proc. of the 2014 EMNLP. 46--56.Google ScholarGoogle ScholarCross RefCross Ref
  34. John M. Swales. 1990. Genre Analysis: English in Academic and Research Settings. Cambridge University Press.Google ScholarGoogle Scholar
  35. Oscar Täckström and Ryan McDonald. 2011. Discovering fine-grained sentiment with latent variable structured prediction models. In Proc. of the 33rd ECIR. 368--374. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Simone Teufel, Advaith Siddharthan, and Colin Batchelor. 2009. Towards discipline-independent argumentative zoning: Evidence from chemistry and computational linguistics. In Proc. of the 2009 EMNLP. 1493--1502. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Stephen E. Toulmin. 1958. The Uses of Argument. Cambridge University Press.Google ScholarGoogle Scholar
  38. Henning Wachsmuth. 2015. Text Analysis Pipelines—Towards Ad-hoc Large-scale Text Mining. Lecture Notes in Computer Science, Vol. 9383. Springer.Google ScholarGoogle Scholar
  39. Henning Wachsmuth, Johannes Kiesel, and Benno Stein. 2015. Sentiment flow -- A general model of web review argumentation. In Proc. of the 2015 EMNLP. 601--611.Google ScholarGoogle ScholarCross RefCross Ref
  40. Henning Wachsmuth, Martin Trenkmann, Benno Stein, and Gregor Engels. 2014a. Modeling review argumentation for robust sentiment analysis. In Proc. of the 25th COLING: Technical Papers. 553--564.Google ScholarGoogle Scholar
  41. Henning Wachsmuth, Martin Trenkmann, Benno Stein, Gregor Engels, and Tsvetomira Palakarska. 2014b. A review corpus for argumentation analysis. In Proc. of the 15th CICLing. 115--127. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Douglas Walton and David M. Godden. 2006. Considering Pragma-Dialectics. Erlbaum, Chapter The Impact of Argumentation on Artificial Intelligence, 287--299.Google ScholarGoogle Scholar
  43. Douglas Walton, Christopher Reed, and Fabrizio Macagno. 2008. Argumentation Schemes. Cambridge University Press.Google ScholarGoogle Scholar

Index Terms

  1. A Universal Model for Discourse-Level Argumentation Analysis

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in

            Full Access

            • Published in

              cover image ACM Transactions on Internet Technology
              ACM Transactions on Internet Technology  Volume 17, Issue 3
              Special Issue on Argumentation in Social Media and Regular Papers
              August 2017
              201 pages
              ISSN:1533-5399
              EISSN:1557-6051
              DOI:10.1145/3106680
              • Editor:
              • Munindar P. Singh
              Issue’s Table of Contents

              Copyright © 2017 ACM

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 12 June 2017
              • Revised: 1 June 2016
              • Accepted: 1 June 2016
              • Received: 1 January 2016
              Published in toit Volume 17, Issue 3

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Qualifiers

              • research-article
              • Research
              • Refereed

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader
            About Cookies On This Site

            We use cookies to ensure that we give you the best experience on our website.

            Learn more

            Got it!