Skip to Main content Skip to Navigation
Theses

Un modèle algorithmique de la généralisation de structures dans le processus d'acquisition du langage

Abstract : The subject of our study is the learning of regular tree languages for an algorithmic modeling of language acquisition. For this, we suppose that data are structured; these data are heard correct sentences and the learning is effective since a representation of the language to which these sentences belong is built. From this representation the learner is able to generate new sentences compatible with the language and not presented as examples. Considering that heard sentences are translated into trees, it appears that the generalization of these tree structures is a component of the learning. We developed several models for this generalization in the form of algorithms taking into account various types of structures as input and various levels of contribution of information. These new models offer the advantage of unifying major results in the theory of the grammatical inference, and of extending these results, in particular by the consideration of new structures not studied previously in the learnability point of view.
Document type :
Theses
File URL :
http://docnum.univ-lorraine.fr/prive/SCD_T_2003_0156_BESOMBES.pdf
Complete list of metadata

https://hal.univ-lorraine.fr/tel-01746828
Contributor : Thèses Ul <>
Submitted on : Thursday, March 29, 2018 - 10:45:36 AM
Last modification on : Tuesday, September 29, 2020 - 3:36:52 PM

Identifiers

  • HAL Id : tel-01746828, version 1

Collections

Citation

Jérôme Besombes. Un modèle algorithmique de la généralisation de structures dans le processus d'acquisition du langage. Autre [cs.OH]. Université Henri Poincaré - Nancy 1, 2003. Français. ⟨NNT : 2003NAN10156⟩. ⟨tel-01746828⟩

Share

Metrics

Record views

23