Construction d'un corpus annoté en genre par apprentissage zero-shot
Résumé
In order to best adapt to new technologies, an association has developed
a webchat application allowing anyone to express and share their
anxieties. Several thousand anonymous conversations have then been
brought together and form an unprecedented corpus of stories about human
distress and social violence. We present in this paper a methodology to
produce a learning model that allows an automatic gender labeling of a
corpus of texts in French. The method is based on a combination of a
Zero-Shot classification algorithm, human validation, and supervised
learning. This method allows us to effectively pre-annotate a large
corpus by presenting some experimental results so that an expert can
finally more easily validate the annotation produced.