Argumentation mining is a relatively new challenge in corpus-based discourse analysis that involves automatically identifying argumentative structures within a document, e.g., the premises, conclusion, and argumentation scheme of each argument, as well as argument-subargument and argument-counterargument relationships between pairs of arguments in the document. To date, researchers have investigated methods for argumentation mining of legal documents (Mochales and Moens 2011; Bach et al. 2013; Ashley and Walker 2013; Wyner et al. 2010), on-line debates (Cabrio and Villata 2012), product reviews (Villalba and Saint-Dizier 2012; Wyner et al. 2012), and newspaper articles and court cases (Feng and Hirst 2011). A related older strand of research (that uses the term ‘argumentative structure’ in a related but different sense than ours) has investigated automatically classifying the sentences of a scientific article’s abstract or full text in terms of their contribution of new knowledge to a field (e.g. Liakata et al. 2012, Teufel 2010, Mizuta et al. 2005). In addition, argumentation mining has ties to sentiment analysis. To date there are few corpora with annotations for argumentation mining research (Reed et al. 2008).
Proposed applications of argumentation mining include improving information retrieval and information extraction, as well as providing end-user visualization and summarization of arguments. Sources of interest include not only formal genres, but also a variety of informal genres such as microtext, spoken meeting transcripts, and product reviews. In instructional contexts where argumentation is a pedagogically important tool for conveying and assessing students’ command of course material, the written and diagrammed arguments of students (and the mappings between them) are educational data that can be mined for purposes of assessment and instruction. This is especially important given the wide-spread adoption of computer-supported peer review, computerized essay grading, and large-scale online courses and MOOCs.
Success in argumentation mining will require interdisciplinary approaches informed by natural language processing technology, theories of semantics, pragmatics and discourse, knowledge of discourse of domains such as law and science, artificial intelligence, argumentation theory, and computational models of argumentation. In addition, it will require creation and annotation of high-quality corpora of argumentation from different types of sources in different domains.
The goal of this workshop is to provide the first research forum devoted to argumentation mining in all domains of discourse. Suggested topics include but are not limited to:
The format of the workshop will consist of presentations of long/short papers, discussion of future meaningful research- and industry-oriented shared tasks and other topics of shared interest, and demos of argument/argumentation mining systems and tools.
We expect that
this workshop will be of interest to many ACL attendees,
including those who might be participating in other ACL
workshops, such as workshops on annotation, information
retrieval, scientific discourse, and BioNLP.
Long paper submissions should follow the two-column format of ACL 2014 proceedings without exceeding eight (8) pages of content plus two extra pages for references. Short paper submissions should also follow the two-column format of ACL 2014 proceedings, and should not exceed four (4) pages plus at most 2 pages for references. Authors who wish to give a demo with their paper presentation should include a brief description of the demo in their paper. People who wish to give a demo without submitting a paper should submit a one-page demo abstract by the deadline. We strongly recommend the use of ACL LaTeX style files or Microsoft Word style files tailored for this year's conference. Submissions must conform to the official style guidelines, which are contained in the ACL style files, and they must be in PDF. (Unlike ACL, blind reviewing is not required; you may include author names.) Submit papers via the START Softconf application. Accepted papers will be freely accessible from the ACL Anthology.
Giuseppe Carenini, U British Columbia
Chrysanne Dimarco, U Waterloo
Floriana Grasso, U Liverpool
Graeme Hirst, U Toronto
Maria Liakata, U Warwick
Collin Lynch, U Pittsburgh
Robert Mercer, U Western Ontario
Raquel Mochales-Palau, Nuance
Patrick Saint-Dizier, Institut de Recherches en Informatique de Toulouse
Manfred Stede, Universitat Potsdam
Joel Tetreault, Yahoo! Labs, USA
Serena Villata, INRIA Sophia-Antipolis Méditerranée
Adam Wyner, U Aberdeen