4th Workshop on Argument Mining, in conjunction with EMNLP 2017
Copenhagen, Denmark; September 8, 2017 https://argmining2017.wordpress.com/
*Submission Deadline – extended: Friday, June 9, 2017*
The goal of the workshop is to provide a follow-on forum to the last three years' Argumentation Mining workshops at ACL, the first research forum devoted to argumentation mining in all domains of discourse.
Argument mining (also, 'argumentation mining', also referred to as 'computational argumentation' in some recent works) is a relatively new challenge in corpus-based discourse analysis that involves automatically identifying argumentative structures within discourse, 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 argument mining in areas such as legal documents, on-line debates, product reviews, academic literature, user comments on proposed regulations, newspaper articles and court cases, as well as in dialogical domains.
Proposed applications of argumentation mining include improving information retrieval and information extraction as well as end-user visualization aiming to a succinct presentation of the pros and cons of a topic of interest. Textual sources of interest include not only statutes, case decisions, and other legal texts, scientific writing and parliamentary records, but also newspaper archives, Wikipedia articles, as well as a variety of informal genres such as microtext, spoken meeting transcripts, product reviews and user comments. 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.
Success in argument 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 the creation and annotation of high-quality corpora of argumentation from different types of sources in different domains.
This workshop will solicit LONG PAPERS and SHORT PAPERS for oral and poster presentations, as well as DEMOS of argument/argumentation mining systems and tools.
Specific topics for submissions include:
o Automatic identification of argument elements (e.g., premises and conclusion; data, claim and warrant), argumentation schemes, relationships between arguments in a document, and relationships to discourse goals (e.g. stages of a 'critical discussion') and/or rhetorical strategies;
o Creation/evaluation of argument annotation schemes, relationship of argument annotation to linguistic and discourse structure annotation schemes, (semi)automatic argument annotation methods and tools, and creation/annotation of high-quality shared argumentation corpora;
o Processing strategies integrating NLP methods and AI models developed for argumentation, such as argumentation frameworks;
o Applications of argument/argumentation mining to, e.g., mining requirements and technical documents, analysis of arguments in dialogue (meetings, etc.), opinion analysis and mining consumer reviews, evaluation of students’ written arguments and argument diagrams, and information access (retrieval, extraction, summarization, and visualization) in scientific and legal documents;
o Argument mining and user generated content (UGC): automatic identification of argument elements in UGC, automatic identification and classification of relations between argument elements, relationships to discourse goals/rhetorical strategies in UGC, manually annotated and applications related to argument mining in UGC;
o Descriptions of implemented systems and tools for argument/argumentation mining;
o Descriptions and proposals for shared tasks;
o Student research proposals.
We will be using the EMNLP 2017 Submission Guidelines (http://emnlp2017.net/call-for-papers.html) for all submissions.
A LONG PAPER submission consists of a paper of up to eight (8) pages of content, plus two pages for references; final versions of long papers will be given one additional page (up to nine pages with unlimited pages for references) so that reviewers’ comments can be taken into account.
A SHORT PAPER submission consists of up to four (4) pages of content, plus 2 pages for references; final versions of short papers will be given one additional page (up to five pages in the proceedings and unlimited pages for references).
Papers that describe systems or tools are also invited to give a DEMO of their system. If you would like to present a demo in addition to presenting the paper, please make sure to select either "full paper + demo" or "short paper + demo" under "Submission Category" in the START submission page.
Previously published papers cannot be accepted. The submissions will be reviewed by the program committee. As reviewing will be blind, please ensure that papers are anonymous. Self-references that reveal the author's identity, e.g., "We previously showed (Smith, 1991) ...", should be avoided. Instead, use citations such as "Smith previously showed (Smith, 1991) ...".
Please use the EMNLP 2017 style sheets for composing your paper:
We will be using the START conference system to manage submissions (link forthcoming).
Submissions Deadline (extended): Friday, June 9, 2017 Notification: Monday, July 10, 2017 Camera-Ready: Friday, July 21, 2017
All deadlines are calculated at 11:59 pm Pacific Daylight Savings Time (UTC-7h).
4th Workshop on Argument Mining: September 8, 2017 (Workshops, Day 2) Main EMNLP conference: September 9-11, 2017
Iryna Gurevych, Technische Universitšt Darmstadt (chair) Ivan Habernal, Technische Universitšt Darmstadt (chair) Kevin Ashley, University of Pittsburgh Claire Cardie, Cornell University Nancy Green, University of North Carolina Greensboro Diane Litman, University of Pittsburgh Georgios Petasis, NCSR Demokritos, Athens Chris Reed, University of Dundee Noam Slonim, IBM Research Vern R. Walker, Maurice A. Deane School of Law at Hofstra University, New York
Please write to: habernal at ukp.informatik.tu-darmstadt.de with any questions.
Stergos Afantenos, University of Toulouse Ahmet Aker, University of Duisburg-Essen Carlos Alzate, IBM Research – Ireland Katarzyna Budzynska, Polish National Academy of Sciences & University of Dundee Elena Cabrio, Université Côte d’Azur, CNRS, Inria, I3S, France Matthias Grabmair, Carnegie Mellon University Graeme Hirst, University of Toronto Eduard Hovy, Carnegie Mellon University Jonas Kuhn, University of Stuttgart Ran Levy, IBM Research Maria Liakata, University of Warwick Beishui Liao, Zhejiang University Marie-Francine Moens, KU Leuven Smaranda Muresan, Columbia University Alexis Palmer, University of North Texas Joonsuk Park, Williams College Simon Parsons, King’s College London Mercer Robert, University of Western Ontario Ariel Rosenfeld, Bar-Ilan University, Israel Patrick Saint-Dizier, IRIT-CNRS Jodi Schneider, University of Illinois at Urbana-Champaign Christian Stab, Technische Universitšt Darmstadt Benno Stein, Bauhaus-Universitšt Weimar Karkaletsis Vangelis, NCSR Demokritos, Athens Serena Villata, CNRS, France Henning Wachsmuth, Bauhaus-Universitšt Weimar Lu Wang, Northeastern University Zhongyu Wei, Fudan University Janyce Wiebe, University of Pittsburgh Adam Wyner, University of Aberdeen
-- ----------------------------------------------------------------------------- Ivan Habernal, Ph.D. Postdoctoral Researcher Ubiquitous Knowledge Processing (UKP) Lab FB 20 / Computer Science Department Technische Universitšt Darmstadt Hochschulstr. 10, D-64289 Darmstadt, Germany Room S2/02/B107 habernal at ukp.informatik.tu-darmstadt.de www.ukp.tu-darmstadt.de
Web Research at TU Darmstadt (WeRC) www.werc.tu-darmstadt.de
Adaptive Preparation of Information from Heterogeneous Sources (AIPHES) GRK 1994 / www.aiphes.tu-darmstadt.de
Knowledge Discovery in Scientific Literature (KDSL) PhD program / www.kdsl.tu-darmstadt.de -----------------------------------------------------------------------------