A Computational Model for Taxonomy-Based Word Learning Inspired by Infant Developmental Word Acquisition

Akira TOYOMURA
Takashi OMORI

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E88-D    No.10    pp.2389-2398
Publication Date: 2005/10/01
Online ISSN: 
DOI: 10.1093/ietisy/e88-d.10.2389
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Biocybernetics, Neurocomputing
Keyword: 
word acquisition,  shape bias,  taxonomic bias,  human interface,  neural network,  PATON,  

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Summary: 
To develop human interfaces such as home information equipment, highly capable word learning ability is required. In particular, in order to realize user-customized and situation-dependent interaction using language, a function is needed that can build new categories online in response to presented objects for an advanced human interface. However, at present, there are few basic studies focusing on the purpose of language acquisition with category formation. In this study, taking hints from an analogy between machine learning and infant developmental word acquisition, we propose a taxonomy-based word-learning model using a neural network. Through computer simulations, we show that our model can build categories and find the name of an object based on categorization.

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