Never Retreat, Never Retract: Argumentation Analysis for Political Speeches - Archive ouverte HAL Access content directly
Conference Papers Year : 2018

Never Retreat, Never Retract: Argumentation Analysis for Political Speeches

Abstract

In this work, we apply argumentation mining techniques, in particular relation prediction, to study political speeches in monological form, where there is no direct interaction between opponents. We argue that this kind of technique can effectively support researchers in history, social and political sciences, which must deal with an increasing amount of data in digital form and need ways to automatically extract and analyse argumentation patterns. We test and discuss our approach based on the analysis of documents issued by R. Nixon and J. F. Kennedy during 1960 presidential campaign. We rely on a supervised classifier to predict argument relations (i.e., support and attack), obtaining an accuracy of 0.72 on a dataset of 1,462 argument pairs. The application of argument mining to such data allows not only to highlight the main points of agreement and disagreement between the candidates' arguments over the campaign issues such as Cuba, disarmament and health-care, but also an in-depth argumentative analysis of the respective viewpoints on these topics.
Fichier principal
Vignette du fichier
16393-76780-1-PB(1).pdf (1.05 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive
Loading...

Dates and versions

hal-01876442 , version 1 (06-08-2019)

Identifiers

  • HAL Id : hal-01876442 , version 1

Cite

Stefano Menini, Elena Cabrio, Sara Tonelli, Serena Villata. Never Retreat, Never Retract: Argumentation Analysis for Political Speeches. AAAI 2018 - 32nd AAAI Conference on Artificial Intelligence, Feb 2018, New Orleans, United States. pp.4889-4896. ⟨hal-01876442⟩
400 View
220 Download

Share

Gmail Facebook X LinkedIn More