Argumentation mining by Raquel Mochales and Marie-Francine Moens, Artificial Intelligence and Law Volume 19, Number 1, 1-22, DOI: 10.1007/s10506-010-9104-x.
Abstract:
Argumentation mining aims to automatically detect, classify and structure argumentation in text. Therefore, argumentation mining is an important part of a complete argumentation analyisis, i.e. understanding the content of serial arguments, their linguistic structure, the relationship between the preceding and following arguments, recognizing the underlying conceptual beliefs, and understanding within the comprehensive coherence of the specific topic. We present different methods to aid argumentation mining, starting with plain argumentation detection and moving forward to a more structural analysis of the detected argumentation. Different state-of-the-art techniques on machine learning and context free grammars are applied to solve the challenges of argumentation mining. We also highlight fundamental questions found during our research and analyse different issues for future research on argumentation mining.
I mention this for two reasons.
First, a close friend of mine thinks tracking argumentation is a way to guide diverse audiences into useful discussions about globally important issues. On the other hand, I have observed as few as seven or eight committee members be unable to find a common place for a lunch break. Perhaps some decisions are harder than others. 😉
Second, and perhaps more pragmatically, I think identification of arguments in texts are an important part of textual analysis, from a scholarly perspective. Any tool that can play the role of assistant in that task, is of interest to me.