Abstract
The objective of our research is to computationally model word production and its disorders by means of artificial neural networks. In the current study we develop and analyze an algorithm that generates a distributed semantic coding from a given semantic tree-structure classification of words. With the algorithm it is possible to generate semantic representations that are compact and easy to modify. This renders the coding method suitable for our multilayer perceptron-based neural network model of word production. The model is shown to be able to account for a variety of performance patterns observed in four Finnish aphasia patients suffering from word-finding difficulties.
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Acknowledgements
The first author wishes to thank the Academy of Finland (project# 78676) and Tampere Graduate School in Information Science and Engineering (TISE) for financial support.
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Järvelin, A., Juhola, M. & Laine, M. Neural network modelling of word production in Finnish: coding semantic and non-semantic features. Neural Comput & Applic 15, 91–104 (2006). https://doi.org/10.1007/s00521-005-0012-z
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DOI: https://doi.org/10.1007/s00521-005-0012-z