The structure of verbal sequences analyzed with unsupervised learning techniques

Catherine Recanati (1) , Nicoleta Rogovschi (1) , Younès Bennani (1)

1 LIPN - Laboratoire d'Informatique de Paris-Nord

Data mining allows the exploration of sequences of phenomena, whereas one usually tends to focus on isolated phenomena or on the relation between two phenomena. It offers invaluable tools for theoretical analyses and exploration of the structure of sentences, texts, dialogues, and speech. We report here the results of an attempt at using it for inspecting sequences of verbs from French accounts of road accidents. This analysis comes from an original approach of unsupervised training allowing the discovery of the structure of sequential data. The entries of the analyzer were only made of the verbs appearing in the sentences. It provided a classification of the links between two successive verbs into four distinct clusters, allowing thus text segmentation. We give here an interpretation of these clusters by applying a statistical analysis to independent semantic annotations.

Mots clés: Time and aspect; discourse semantics; Natural language processing; text typology; data mining; unsupervised learning

Domaines: Informatique et langage [cs.CL]; Linguistique; Apprentissage [cs.LG]; Intelligence artificielle [cs.AI]

Catherine Recanati, Nicoleta Rogovschi, Younès Bennani. The structure of verbal sequences analyzed with unsupervised learning techniques. The 3rd Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, Oct 2007, Poznan, Poland. pp. 325-329. ⟨hal-00160804⟩