Connectionist modeling of linguistic quantifiers

Authors:
Rohana K. Rajapakse;Angelo Cangelosi;Kenny R. Coventry;Steve Newstead;Alison Bacon
Affiliations:
School of Computing Comms and Electronics, University of Plymouth, Plymouth, UK;School of Computing Comms and Electronics, University of Plymouth, Plymouth, UK;Centre for Thinking & Language, School of Psychology, University of Plymouth, Plymouth, UK;Centre for Thinking & Language, School of Psychology, University of Plymouth, Plymouth, UK;Centre for Thinking & Language, School of Psychology, University of Plymouth, Plymouth, UK
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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Abstract

This paper presents a new connectionist model of the grounding of linguistic quantifiers in perception that takes into consideration the contextual factors affecting the use of vague quantifiers. A preliminary validation of the model is presented through the training and testing of the model with experimental data on the rating of quantifiers. The model is able to perform the "psychological" counting of objects (fish) in visual scenes and to select the quantifier that best describes the scene, as in psychological experiments.