Work

From Political Debates to Deliberative Democracy: A Roadmap to Assess Semi-Supervised Argument Mining with DISPUTool

Year: 2024

Type: conference-paper

Abstract: Argument mining is a field in natural language processing that studies the automatic extraction of arguments and the classification of their structure from free text. This is a promising research area with numerous applications, like fact-checking, qualitative assessments of online debates, or analysis of legal documents, but with a set of nontrivial challenges to overcome, the main one being the lack of large language resources for the development of this kind of model. In this work, we propose a research path on a subarea of argument mining that has not been explored as much as some other areas: semi-supervised argument mining. We will explore the adaptation of DISPUTool, a model originally trained on data from the US Presidential Elections Political Debates, to deliberative democracy debates within the ORBIS Project for “augmenting participation, co-creation, trust and transparency in deliberative democracy at all scales”. Our discussion highlights the technical and non-technical challenges of this task and our planned path of action to overcome them.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.13789191

Authors: Cabrio, Elena, Villata, Serena, Cardellino, Cristian

Institutions: Université Côte d'Azur, Centre National de la Recherche Scientifique

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Peace, justice, and strong institutions

Open Access status: green

Funder: European Commission