Prédiction automatique de fonctions pragmatiques dans les reformulations

Abstract : Automatic prediction of pragmatic functions in reformulations. Reformulations participate in structuring of discourse, especially in dialogues, and also contributes to the dynamics of the discourse. Reformulation is a significant act which has to satisfy precise objectives. The purpose of our work is to automatically predict the reason for which a speaker performs a reformulation. We use a classification with eleven pragmatic functions inspired by the existing work and by the data analyzed. The reference data are built through manual and consensual annotations of spontaneous reformulations introduced by three markers (c'est-à-dire, je veux dire, disons). The data are provided by spoken corpora and a corpus with forum discussions on health issues. We exploit supervised categorization algorithms and a set with several descriptors (syntactic, formal, semantic and discursive) for the prediction of the reformulation categories. The distribution of utterances and sentences is not homogeneous across categories. The experiments are positioned at two levels : general and specific. Our results indicate that it is easier to predict the types of functions at the general level (the average F-measure is around 0.80), than at the level of individual categories (the average F-measure is around 0.40). We study the influence of various parameters.
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Natalia Grabar, Iris Eshkol-Taravella. Prédiction automatique de fonctions pragmatiques dans les reformulations. TALN 2016: Traitement Automatique des Langues Naturelles, Jun 2016, Paris, France. ⟨hal-01426814⟩

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