Abstract
The automatic extraction of arguments from text, also known as argument mining, has recently become a hot topic in artificial intelligence. Current research has only focused on linguistic analysis. However, in many domains where communication may be also vocal or visual, paralinguistic features too may contribute to the transmission of the message that arguments intend to convey. For example, in political debates a crucial role is played by speech. The research question we address in this work is whether in such domains one can improve claim detection for argument mining, by employing features from text and speech in combination. To explore this hypothesis, we develop a machine learning classifier and train it on an original dataset based on the 2015 UK political elections debate.
Marco Lippi
Associate Professor
He is an expert in machine learning and deep learning, with applications in many domains including natural language processing.
Paolo Torroni
Associate Professor
Head of the Language Technologies lab. His main research focus is in artificial intelligence, and in particular natural language processing, multi-agent systems, and computational logics.