A Computational Model for Taxonomy-Based Word Learning Inspired by Infant Developmental Word Acquisition
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E88-D No.10 pp.2389-2398
- Publication Date
- 2005/10/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietisy/e88-d.10.2389
- Type of Manuscript
- PAPER
- Category
- Biocybernetics, Neurocomputing
Authors
Keyword
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Akira TOYOMURA, Takashi OMORI, "A Computational Model for Taxonomy-Based Word Learning Inspired by Infant Developmental Word Acquisition" in IEICE TRANSACTIONS on Information,
vol. E88-D, no. 10, pp. 2389-2398, October 2005, doi: 10.1093/ietisy/e88-d.10.2389.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e88-d.10.2389/_p
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@ARTICLE{e88-d_10_2389,
author={Akira TOYOMURA, Takashi OMORI, },
journal={IEICE TRANSACTIONS on Information},
title={A Computational Model for Taxonomy-Based Word Learning Inspired by Infant Developmental Word Acquisition},
year={2005},
volume={E88-D},
number={10},
pages={2389-2398},
abstract={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.},
keywords={},
doi={10.1093/ietisy/e88-d.10.2389},
ISSN={},
month={October},}
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TY - JOUR
TI - A Computational Model for Taxonomy-Based Word Learning Inspired by Infant Developmental Word Acquisition
T2 - IEICE TRANSACTIONS on Information
SP - 2389
EP - 2398
AU - Akira TOYOMURA
AU - Takashi OMORI
PY - 2005
DO - 10.1093/ietisy/e88-d.10.2389
JO - IEICE TRANSACTIONS on Information
SN -
VL - E88-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2005
AB - 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.
ER -
English
