The task of argumenation mining involves identifying the different aspects of the argumentation structure of a text, i.e. finding the central claim of a text, supporting reasons, possible objections and counters to those objections. For us, the overall goal is to integrate the different argumentative elements and relations in a global argumentation structure spanning over the whole text. For many practical purposes, though, only certain subtasks are needed.
A variety of applications involving automatic text processing can profit from access to the argumentative structure of text, such as: the retrieval of relevant court decisions from legal databases, the analysis of scientific papers in biomedical text mining, automatic document summarization systems, essay scoring systems, as well as opinion mining applications, not only for commercial purposes, but also as a tool for assessing public opinion in political decision-making. To make argumentation structures available for these applications, robust automatic argument recognition is required, based on resources that have been created in a reproducible fashion with a coding scheme that can be reliably applied.
Our new papers on argumentation appear in the Argumentation and Computation journal 2018, at the Coling 2018 and LREC 2018 conferences, and in a book chapter on Health Technology. Also, a book on Argumentation Mining written by Manfred Stede and Jodi Schneider will appear in the autumn of 2018. See the references at the bottom of the page.
The argumentation structure of a text is a graph representation of the argumentative relations between the propositions expressed in the segments of the text (i.e. typically sentences or clauses). It identifies the central claim of the text, supporting premises, possible objections and counters to these objections.
We devised a scheme for annotating such argumentation structures (Peldszus Stede 2013), and worked on the automatic recognition of those structures via joint optimization (Peldszus/Stede 2015, Afantenos et al. 2018).
Fig. 1: An example argumentation structure
Semi-automatic Analysis of Arguments in Social Media
To find recurring arguments in debates on a certain issue, we have constructed a tool with which computer-assisted argument extraction from large text collections can be carried out. The tool (i) automatically selects a subset of the text collection that contains re-occurring topics, to minimise the amount of text that the human coder has to read, and (ii) presents selected texts and extracted information in a graphical user interface that facilitates manual coding of arguments. A video demo of the tool can be found here.
In collaboration with colleagues in Weimar, we started work on re-synthesizing new argumentative text, using the microtexts pertaining to a common topic as input. Our first step is a human annotation study, presented in (Wachsmuth et al. 2018).
We distribute a corpus of short argumentative texts (parallel in English and German; annotated according to the scheme mentioned above) and present results on automatically recognizing the argumentation structures (e.g., Peldszus/Stede 2015, Afantenos et al. 2018). The corpus has also been annotated with other layers of linguistic information, as explained on the corpus page.
Further, we offer the annotation tool that has been used for building the argumentation structure graphs in the microtext corpus: (GraPAT).
Fig. 2: Annotating argumentation structure in GraPAT
- arg-microtexts: A German English parallel corpus of short argumentative texts annotated with argumentation structures
- GraPAT: A graph-based, web-based annotation tool suited for sentiment and argumentation structure annotation
- Elena Musi, Tariq Alhindi, Manfred Stede, Leonard Kriese, Smaranda Muresan, and Andrea Rocci. A multi-layer annotated corpus of argumentative text: from argument schemes to discourse relations. In N. Calzolari et al., editor, Proceedings of the 11h International Conference on Language Resources and Evaluation (LREC'18). Miyazaki, Japan, 2018. European Language Resources Association (ELRA). [Bibtex]
- Stergos Afantenos, Andreas Peldszus, and Manfred Stede. Comparing decoding mechanisms for parsing argumentative structures. Argument and Computation, 2018. URL: https://content.iospress.com/articles/argument-and-computation/aac033. [Bibtex]
- Henning Wachsmuth, Manfred Stede, Roxanne El Baff, Khalid Al Khatib, Maria Skeppstedt, and Benno Stein. Argumentation synthesis following rhetorical strategies. In Proceedings of COLING 2018. Santa Fe, NM, USA, 2018. To appear. [Bibtex]
- Manfred Stede and Jodi Schneider. Argumentation Mining volume of Synthesis Lectures in Human Language Technology. Morgan & Claypool, 2018. To appear. [Bibtex]
- Maria Skeppstedt, Kostiantyn Kucher, Manfred Stede, and Andreas Kerren. Topics2Themes: Computer-Assisted Argument Extraction by Visual Analysis of Important Topics. In Proceedings of the LREC Workshop on Visualization as Added Value in the Development, Use and Evaluation of Language Resources, 9–16. 2018. [Bibtex]
- Maria Skeppstedt, Andreas Kerren, and Manfred Stede. Vaccine hesitancy in discussion forums : computer-assisted argument mining with topic models. In Building Continents of Knowledge in Oceans of Data : The Future of Co-Created eHealth, number 247 in Studies in Health Technology and Informatics, 366–370. IOS Press, 2018. [Bibtex]
- Katarina Krüger, Anna Lukowiak, Jonathan Sonntag, and Manfred Stede. Classifying news versus opinions in newspapers: Linguistic features for domain independence. Natural Language Enginnering, 23(5):687–707, 2017. URL: http://dx.doi.org/10.1017/S1351324917000043. [Bibtex]
- Patrick Saint-Dizier and Manfred Stede. Foundations of the Language of Argumentation. Special Issue of `Argument and Computation' volume 8(2). IOS Press, 2017. URL: http://content.iospress.com/journals/argument-and-computation/8/2. [Bibtex]
- Pietro Baroni, Thomas F. Gordon, Tatjana Scheffler, and Manfred Stede. Computational Models of Argument: Proceedings of COMMA 2016 volume 287 of Frontiers in Artificial Intelligence and Applications. IOS Press, 2016. [Bibtex] [PDF]
- Andreas Peldszus and Manfred Stede. An annotated corpus of argumentative microtexts. In D. Mohammed, and M. Lewinski, editors, Argumentation and Reasoned Action - Proc. of the 1st European Conference on Argumentation, Lisbon, 2015. College Publications, London, 2016. [Bibtex]
- K. Budzynska,, M. Janier, B. Konat, J. Kang, C. Reed, P. Saint-Dizier, M. Stede, and O. Yaskorska. Automatically identifying transitions between locutions in dialogue. In D. Mohammed, and M. Lewinski, editors, Argumentation and Reasoned Action - Proc. of the 1st European Conference on Argumentation, Lisbon, 2015. College Publications, London, 2016. [Bibtex]
- Andreas Peldszus and Manfred Stede. Rhetorical structure and argumentation structure in monologue text. In Proceedings of the 3rd Workshop on Argumentation Mining. Berlin, September 2016. Association for Computational Linguistics. [Bibtex] [PDF]
- Tatjana Scheffler and Manfred Stede. Realizing argumentative coherence relations in German: a contrastive study of newspaper editorials and Twitter posts. In Proceedings of the COMMA Workshop "Foundations of the Language of Argumentation". Potsdam, Germany, 2016. [Bibtex] [PDF]
- Manfred Stede. Toward assessing depth of argumentation. In Proceedings of COLING 2016. Osaka, Japan, 2016. [Bibtex] [PDF]
- Andreas Peldszus and Manfred Stede. Towards detecting counter-considerations in text. In Proceedings of the 2nd Workshop on Argumentation Mining, 104–109. Denver, CO, June 2015. Association for Computational Linguistics. [Bibtex] [PDF]
- Andreas Peldszus and Manfred Stede. Joint prediction in MST-style discourse parsing for argumentation mining. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), 938–948. Lisbon, Portugal, September 2015. Association for Computational Linguistics. [Bibtex] [PDF]
- K. Budzynska, M. Janier, J. Kang, C. Reed, P. Saint-Dizier, M. Stede, and O. Yakorska. Towards argument mining from dialogue. In Proc. of the Fifth Int'l Conference on Computational Models of Argument (COMMA). 2014. [Bibtex]
- Andreas Peldszus. Towards segment-based recognition of argumentation structure in short texts. In Proceedings of the First Workshop on Argumentation Mining, 88–97. Baltimore, Maryland, June 2014. Association for Computational Linguistics. [Bibtex] [PDF]
- Andreas Peldszus and Manfred Stede. From argument diagrams to argumentation mining in texts: a survey. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 7(1):1–31, 2013. [Bibtex] [DOI] [PDF]
- Andreas Peldszus and Manfred Stede. Ranking the annotators: an agreement study on argumentation structure. In Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, 196–204. Sofia, Bulgaria, August 2013. Association for Computational Linguistics. [Bibtex] [PDF]
- Manfred Stede and Antje Sauermann. Linearization of arguments in commentary text. In Proceedings of the Workshop on Multidisciplinary Approaches to Discourse (MAD). Oslo, 2008. [Bibtex]