Argument Discovery and Extraction with the Argument Workbench

Proceedings of the 2nd Workshop on Argumentation Mining, pages 78–83, Denver, Colorado, June 4, 2015. c⃝2015 Association for Computational Linguistics Argument Discovery and Extraction with the Argument Workbench Adam Wyner Computing Science University of Aberdeen Aberdeen, United Kingdom azwyner@abdn.ac.uk Wim Peters Computer Science University of Sheffield Sheffield, United Kingdom w.peters@sheffield.ac.uk David Price DebateGraph United Kingdom david@debategraph.org Abstract The paper discusses the architecture and de- velopment of an Argument Workbench, which is a interactive, integrated, modular tool set to extract, reconstruct, and visualise arguments. We consider a corpora with dispersed infor- mation across texts, making it essential to con- ceptually search for argument elements, top- ics, and terminology. The Argument Work- bench is a processing cascade, developed in collaboration with DebateGraph. The tool supports an argument engineer to reconstruct arguments from textual sources, using infor- mation processed at one stage as input to a subsequent stage of analysis, and then build- ing an argument graph. We harvest and pre- process comments; highlight argument indi- cators, speech act and epistemic terminology; model topics; and identify domain terminol- ogy. We use conceptual semantic search over the corpus to extract sentences relative to argu- ment and domain terminology. The argument engineer uses the extracts for the construction of arguments in DebateGraph. 1 Introduction Argumentative text is rich, multidimensional, and fine-grained, consisting of (among others): a range of (explicit and implicit) discourse relations between statements in the corpus, including indicators for conclusions and premises; speech acts and proposi- tional attitudes; contrasting sentiment terminology; and domain terminology. Moreover, linguistic ex- pression is various, given alternative syntactic or lexical forms for related semantic meaning. It is dif- ficult for humans to reconstruct argument from text, let alone for a computer. This is especially the case where arguments are dispersed across unstructured textual corpora. In our view, the most productive scenario is one in which a human argument engineer is maximally assisted in her work by computational means in the form of automated text filtering and an- notation. This enables the engineer to focus on text that matters and further explore the argumentation structure on the basis of the added metadata. The Argument WorkBench (AWB) captures this process of incremental refinement and extension of the argu- ment structure, which the engineer then produces as a structured object with a visual representation. Given the abundance of textual source data avail- able for argumentation analysis there is a real need for automated filtering and interpretation. Cur- rent social media platforms provide an unprece- dented source of user-contributed content on most any topic. Reader-contributed comments to a com- ment forum, e.g. for a news article, are a source of arguments for and against issues raised in the article, where an argument is a claim with justifications and exceptions. It is difficult to coherently understand the overall, integrated meaning of the comments. To reconstruct the arguments sensibly and reusably, we build on a prototype Argument Work- bench (AWB) (Wyner et al.(2012); Wyner(2015)), which is a semi-automated, interactive, integrated, modular tool set to …

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