Proceedings of the First Workshop on Argumentation Mining

Abstract

ii Introduction 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 argumentcounterargument relationships between pairs of arguments. 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:• 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;• 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;• Processing strategies integrating NLP methods and AI models developed for argumentation such as argumentation frameworks; and• 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. AbstractAutomated argumentation mining requires an adequate type system or annotation scheme for classifying the patterns of argument that succeed or fail in a corpus of le...

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