When questions say more than answers: automatic classification of intentions in questions

This work aims to automatically detect the intents of speakers in questions asked during meals. The corpus is composed of transcripts of spontaneous oral conversations. We suggest a typology of these intents based on existing work and the corpus analysis. We implement a supervised automatic classification model based on annotated data and selected linguistic features, whose results and performance are evaluated and interpreted.