A Computational Model for Children's Language Acquisition Using Inductive Logic Programming

Authors:
Ikuo Kobayashi;Koichi Furukawa;Tomonobu Ozaki;Mutsumi Imai
Affiliations:
-;-;-;-
Venue:
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Year:
2002

Citing 1
Cited 1

Machine Learning

Machine Learning

Evaluation of a system for noun concepts acquisition from utterances about images (SINCA) using daily conversation data

NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

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Abstract

This paper describes our research activity on developing a computational model for children's word acquisition using inductive logic programming. We incorporate cognitive biases developed recently to explain the efficiency of children's language acquisition. We also design a co-evolution mechanism of acquiring concept definitions for words and developing concept hierarchy. Concept hierarchy plays an important role of defining contexts for later word learning processes. A context switching mechanism is used to select relevant set of attributes for learning a word depending on the category which it belongs to. On the other hand, during acquiring definitions for words, concept hierarchy is developed. We developed an experimental language acquisition system called WISDOM (Word Induction System for Deriving Object Model) and conducted virtual experiments or simulations on acquisition of words in two different categories. The experiments shows feasibility of our approach.