Towards a Bio-computational Model of Natural Language Learning

International audienceThis paper tries to bring together the theory of Grammatical Inference and the studies of natural language acquisition. We discuss how the studies of natural language acquisition can improve results in the field of Grammatical Inference, and how a computational model inspired by such studies can help to answer several key questions in natural language learning

A Grammatical Inference Model for Measuring Language Complexity

International audienc

A Proposal for Contextual Grammatical Inference

International audienc

Bio-inspired Grammatical Inference

International audienceThe field of Grammatical Inference was originally motivated by the problem of natural language acquisition. However, the formal models proposed within this field have left aside this linguistic motivation. In this paper, we propose to improve models and techniques used in Grammatical Inference by using ideas coming from linguistic studies. In that way, we try to give a new bio-inspiration to this field

A First-Order-Logic Based Model for Grounded Language Learning

International audienc

Iterative learning of simple external contextual languages

10.1007/978-3-540-87987-9_30Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)5254 LNAI359-37

Learning Finite Automata Using Label Queries

International audienc

How can Grammatical Inference Contribute to Computational Linguistics?

International audienc