Arguing is a fundamental aspect of human communication and reasoning. We may argue with our friends about which movie to watch, with experts about the long-term effects of global warming, or with ourselves about which of various job offers to take. But what exactly is an argument? What components is it made of? What is the interplay between different arguments? And what makes an argument convincing?
In recent years, Natural Language Processing (NLP) and Machine Learning methods have been applied to try and answer these question, resulting in a new research area called “Argument Mining”. This seminar will introduce some theoretical ideas and assumptions underlying many of the works in Argument Mining and review in depth the latest research in the field.
Topics covered in the seminar are, among others, the automatic detection of claims and evidence in text, the identification of contradictory and supporting arguments in debates, how to predict the convincingness of arguments, and how to judge if two arguments are making the same point. We will also discuss exciting applications of Argument Mining research, including detecting deceptive reviews, predicting the winner of a debate, and automatically grading essays.