ANTHAPSI : un système d'analyse thématique et d'apprentissage de connaissances pragmatiques fondé sur l'amorçage

Abstract : The ANTHAPSI system aims at automatically learning knowledge about prototypical situations of the world from texts. For making this learning process the most general as possible, the use of a priori pragmatic knowledge in ANTHAPSI is reduced as much as possible. In order to reach its goal, ANTHAPSI stresses the necessity for tightly associating text analysis, here a thematic analysis, and knowledge learning. This association relies on a particular memory model, in charge of linking the two processes. This memory actively stores the knowledge that is necessary for the thematic analysis to work and that is produced by the learning process from the text representations built by the thematic analysis. This way, the association between thematic analysis and knowledge learning makes an initial core of knowledge to expand possible and implements the first form of bootstrapping of ANTHAPSI, which is called in-level boostrapping. ANTHAPSI also tackles the way this initial core can be built from less precise and less structured knowledge. Thus, it is composed of two parts which are similar on the functional point of view but use knowledge and representations of two different levels: MLK applies the principles of the in-level boostrapping by using representations made of conceptual graphs while ROSA applies the same principles but with representations only made of words. The inter-level boostrapping here shows more specifically how ROSA can make MLK start working, at least on the thematic point of view.
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Olivier Ferret. ANTHAPSI : un système d'analyse thématique et d'apprentissage de connaissances pragmatiques fondé sur l'amorçage. Interface homme-machine [cs.HC]. Université Paris Sud - Paris XI, 1998. Français. ⟨tel-00189116⟩

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