Extraction automatique de cadres de sous-catégorisation verbale pour le français à partir d'un corpus arboré

Abstract

International audienceWe present our work on automatic extraction of subcategorisation frames for 1362 French verbs. We use a treebank of 15000 sentences from which we extract 12510 verb occurrences. We evaluate the results based on a functional representation of frames and we acquire 39 different frames, 1.54 per lemma on average. Then, we adopt a mixed representation (functions and categories), which leads to 925 different frames, 3.44 frames on average. We investigate several methods to reduce the ambiguity (e.g., neutralisation of passive forms or clitic arguments), which allows us to arrive at 235 frames, with 1.94 frames per lemma on average. We present a brief comparison with the existing work on French and English