Detection of Reformulations in Spoken French

Natalia Grabar (1) , Iris Eshkol-Taravela (2)

1 STL - Savoirs, Textes, Langage (STL) - UMR 8163
2 LLL - Laboratoire Ligérien de Linguistique

Our work addresses automatic detection of enunciations and segments with reformulations in French spoken corpora. The proposed approach is syntagmatic. It is based on reformulation markers and specificities of spoken language. The reference data are built manually and have gone through consensus. Automatic methods, based on rules and CRF machine learning, are proposed in order to detect the enunciations and segments that contain reformulations. With the CRF models, different features are exploited within a window of various sizes. Detection of enunciations with reformulations shows up to 0.66 precision. The tests performed for the detection of reformulated segments indicate that the task remains difficult. The best average performance values reach up to 0.65 F-measure, 0.75 precision, and 0.63 recall. We have several perspectives to this work for improving the detection of reformulated segments and for studying the data from other points of view.

Mots clés: Spoken Corpora; Reformulation; Reformulation Marker; Paraphrase; Supervised Machine Learning

Domaines: Traitement du texte et du document; Linguistique

Natalia Grabar, Iris Eshkol-Taravela. Detection of Reformulations in Spoken French. LREC (Language Resources and Evaluation Conference) 2016, May 2016, Portorož, Slovenia. ⟨hal-01426788⟩