Detecting topic shifts using a cache memory

Brigitte Bigi (1) , Renato de Mori (1) , Marc El-Bèze (1) , Thierry Spriet (1)

1 LIA - Laboratoire Informatique d'Avignon

The use of cache memories and symmetric Kullback-Leibler distances is proposed for topic classification and topic-shift detection. Experiments with a large corpus of articles from the French newspaper "Le Monde show tangible advantages when different models are combined with a suitable strategy. Experimental results show that different strategies for topic shift detection have to be used depending on whether high recall or high precision are sought. Furthermore, methods based on topic independent distributions provide complementary candidates with respect to the use of topic-dependent distributions leading to an increase in recall with a minor loss in precision.

Mots clés: Language modelling; Topic identification; Speech

Domaines: Informatique et langage [cs.CL]; Sciences de l'information et de la communication

Brigitte Bigi, Renato de Mori, Marc El-Bèze, Thierry Spriet. Detecting topic shifts using a cache memory. 5th International Conference on Spoken Language Processing, 1998, Sydnay, Australia. pp.2331-2334. ⟨hal-01392226⟩