Hiroyuki Kido: Argumentation Mining from Acceptability of Arguments
西溪逻辑论坛第115期
Date: 15 May 2019 (9:30-11:30)
Venue: Room 259, Main Teaching Building, Xixi Campus, Zhejiang University
Speaker: Dr. Hiroyuki Kido (Sun Yat-sen University)
Title: Argumentation Mining from Acceptability of Arguments
Abstract:
Argumentation mining aims to identify an argumentative structure from unstructured data consisting mainly of natural language texts. The most challenging task of argumentation mining is considered to be a prediction of an attack relation between arguments. Its dominant approach is currently natural language processing (NLP) with machine learning. However, it is too optimistic to expect that NLP solves it perfectly. In my talk, the task is tackled by a combination of Bayesian statistics and computational argumentation. I present a generative model to solve an inverse problem of argument-based reasoning. Given noisy sentiments regarding acceptability of arguments, the inverse problem finds an attack relation explaining the sentiments in terms of acceptability semantics. It thus statistically constructs a justification for the sentiments. The results of an experiment support our hypothesis that sentiments collected in an online forum can be reasonably predicted through an attack relation estimated with our generative model.
