Two key tasks in argument mining (AM) are classification of argument components and identification of relations between argument components. Approaches to solving the argument component classification problem typically take a supervised learning approach, however a lack of suitable datasets makes this a challenge for identification of argument component relations. We propose a pipeline with a recurrent, branched structure that combines supervised learning of argument component classifications with NLP approaches to identification of argument component relations, with the aim of improving both classification of argument components (i.e. premises and claims) and identification of support relationships between components.
|Number of pages||5|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - Nov 2019|
|Event||3rd Workshop on Advances In Argumentation In Artificial Intelligence, AI^3 2019 - Rende, Italy|
Duration: 19 Nov 2019 → 22 Nov 2019
- Argument mining
- Computational argumentation