Combining classification-centered and relation-based argument mining methods

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Pages (from-to)135-139
Number of pages5
JournalCEUR Workshop Proceedings
Publication statusPublished - Nov 2019
Event3rd Workshop on Advances In Argumentation In Artificial Intelligence, AI^3 2019 - Rende, Italy
Duration: 19 Nov 201922 Nov 2019


  • Argument mining
  • Computational argumentation


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