An Informatics Perspective on Argumentation Mining.
Jodi Schneider · CEUR Workshop Proceedings · 2014
It is time to develop a community research agenda in argumentation mining. I suggest some questions to drive a joint community research agenda and then explain how my research in argumentation, on support tools and knowledge representations, advances argumentation mining. 1 Time for a community research agenda This year, argumentation mining is receiving significant attention. Five different events from April to July 2014 focus on topics such as arguing on the Web, argumentation theory and natural language processing, and argumentation mining. A coordinated research agenda could help advance this work in a systematic way. We have not yet agreed on the most fundamental issues: Q1 What counts as ‘argumentation’, in the context of the argumentation mining task? Q2 How do we measure the success of an argumentation mining task? (e.g. corpora & gold standards) “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.” (Green et al., 2014) ⇤This work was carried out during the tenure of an ERCIM “Alain Bensoussan” Fellowship Programme. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/20072013) under grant agreement n 246016. An informatics perspective (i.e. concerned with supporting human activity) could help us understanding how we will apply argumentation mining; this should sharpen the definition of the argumentation mining task(s). Given such an operationalization, we can then use the standard natural language processing approach: define a corpus of interest, make a gold standard annotation, test algorithms, iterate... For instance, to operationalize the definition of argumentation mining (Q1), we need to know: Q1a How do we plan to use the results of argumentation mining? Q1b What domain(s) and human tasks are to be supported? Q1c What is the appropriate level of granularity of argument structures in a given context? Which models of argumentation are most appropriate? This can be challenging because argumentation has a variety of meanings and uses, in fields from philosophy to rhetoric to law; some of the purposes for using argumentation are shown in Figure 1. Understanding how we will use the results of argumentation mining can help address important questions related to Q2, such as measuring the success of algorithms and support tools for identifying arguments. In particular: Q2a How accurate does argumentation mining need to be? Q2b In which applications are algorithms for automatically extracting argumentation most appropriate? Q2c In which applications are support tools for semi-automatically extracting argumentation more appropriate? In my work I have tried to bring applications of argumentation mining to the forefront. My work falls into three main areas: supporting human argumentation with computer tools (CSCW), repFigure 1: Argumentation can be used for many purposes. Download an editable version of this figure from FigShare DOI http://dx.doi.org/10.6084/m9.figshare.1149925 resenting argumentation in ontologies (knowledge representation), and mining arguments from social media (information extraction using argumentation theory). 2 Computer-Supported Collaborative