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Communication Dans Un Congrès Année : 2022

Organizing debate, debating organization

Mohamed Chenene
  • Fonction : Auteur
  • PersonId : 1221819
Olga Kavvada
  • Fonction : Auteur
  • PersonId : 1221820
Jean Danielou
  • Fonction : Auteur
  • PersonId : 1221821
Phillipe Calvez
  • Fonction : Auteur
  • PersonId : 1221822

Résumé

Controversy analysis is a broad topic where the opinions of different stakeholders are analyzed to identify the various arguments that are stated and classify the positions taken on the subject. Gathering this data can be very time-consuming to do manually and usually is error-prone and not exhaustive. Automated text classification can enhance this process and make it possible to analyze controversial topics by analyzing lists of relevant documents in a short timeframe. In this paper, we propose a 2-step approach to optimize the extraction and classification of arguments in textual data from controversial topics. First, we extract the most relevant paragraphs for the controversy with a retrieval model and then we use an argument mining model to find and classify the relevant arguments. With this method, we are able to successfully characterize long documents and understand the various opinions that are recorded.
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Dates et versions

hal-03966522 , version 1 (31-01-2023)

Identifiants

  • HAL Id : hal-03966522 , version 1

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Mohamed Chenene, Olga Kavvada, Jean Danielou, Phillipe Calvez. Organizing debate, debating organization. Congrès Lambda Mu 23 « Innovations et maîtrise des risques pour un avenir durable » - 23e Congrès de Maîtrise des Risques et de Sûreté de Fonctionnement, Institut pour la Maîtrise des Risques, Oct 2022, Paris Saclay, France. ⟨hal-03966522⟩
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