Fiche du document
16 juin 2020
- handle: 10670/1.bijjsg
- hal: hal-03234268
info:eu-repo/semantics/OpenAccess
Sujets proches
Learning process Scholarship LearningCiter ce document
Céline Poudat, « Apprendre et mesurer la conflictualité avec le deep learning ? », HAL-SHS : linguistique, ID : 10670/1.bijjsg
Métriques
Partage / Export
Résumé
This paper focuses on the linguistic expression of conflicts in Wikipedia. I am currently working on a pragmatic annotation task. I concentrate on the markers of disagreement and conflict (Poudat 2018, Poudat and Ho-Dac 2019) keeping in mind an objective of operative and partially automated description. In this context, I am naturally interested in the possibility of detecting disagreement and conflict in discussions. Deep learning and in particular the convolutional model (CNN) that my research team is currently implementing in Hyperbase Web (Vanni et al. 2018a&b) is very attractive for a number of reasons: on the one hand, it will allow me to assess conflict detection in discussion sequences and threads ; and on the other hand, to enrich the description and annotation of conflicts with new patterns and regularities using the TDS index.