GTM: the generative topographic mapping
Neural Computation
Self-Organizing Maps
A Framework for Representing Knowledge
A Framework for Representing Knowledge
Automatic semantic classification for Chinese unknown compound nouns
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Early lexical development in a self-organizing neural network
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Modern Applied Statistics with S
Modern Applied Statistics with S
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A morphological family in Chinese is the set of compound words embedding a common morpheme. Self-organizing maps (SOM) of Chinese morphological families are built. Computation of the unified-distance matrices for the SOMs allows us to perform a semantic clustering of the members of the morphological families. Such a semantic clustering shed light on the interplay between morphology and semantics in Chinese. Then, we studied how the word lists used in a lexical decision task (LDT) [1] are mapped onto the clusters of the SOMs. We showed that such a mapping is helpful to predict whether in a LDT repetitive processing of members of a morphological family would elicit a satiation - habituation - of both morphological and semantic units of the shared morpheme. In their LDT experiment, [1] found evidence for morphological satiation but not for semantic satiation. Conclusions drawn from our computational experimentations and calculations are concordant with [1] behavioral experimental results. We finally showed that our work could be helpful to linguists to prepare adequate word lists for the behavioral study of Chinese morphological families.