Argument Mining from Speech: Detecting Claims in Political Debates


  • Marco Lippi University of Bologna
  • Paolo Torroni University of Bologna



Argumentation mining, claim detection, argumentation, speech processing, political debate, natural language processing, paralinguistic feature analysis


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.




How to Cite

Lippi, M., & Torroni, P. (2016). Argument Mining from Speech: Detecting Claims in Political Debates. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1).



Technical Papers: NLP and Text Mining