Proceedings of the 4th Workshop on Argument Mining

Abstract

ii IntroductionThe goal of this workshop is to provide a follow-on forum to the last three years' Argumentation Mining workshops at ACL and NAACL, the first research forum devoted to argumentation mining in all domains of discourse.Argument mining (also, "argumentation mining", 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 argumentcounterargument 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. To date there are few corpora with annotations for argumentation mining research although corpora with annotations for argument sub-components have recently become available.Proposed applications of argumentation mining include improving information retrieval and information extraction as well as end-user visualization and summarization of arguments. Textual sources of interest include not only the formal writing of legal text, scientific writing and parliamentary records, but also 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. 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 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.We are looking forward to a full day workshop to exchange ideas and present ongoing research on all of the above -see you all in Copenhagen, Denmark at EMNLP 2017! AbstractIn this paper we present the dataset of 200,000+ political arguments produced in the local phase of the 2016 Chilean constitutional process. We describe the human processing of this data by the government officials, and the manual tagging of arguments performed by members of our research group. Afterwards we focus on classification tasks that mimic the human processes, comparing linear methods ...