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MODELING CONNECTIONS OF SEMANTIC FIELDS IN MENTAL LEXICON

E.V. Erofeeva, K. I. Belousov, Y. E. Leshchenko

First published: 2017https://doi.org/10.5593/sgemsocial2017/32/S14.109View metrics

Abstract

Mental lexicon is a complex system that reflects in a linguistic form the processes of cognition and structuring the surrounding reality. Mental lexicon can be represented in form of a multidimensional network; its structural units are nodes (fragments of information fixed in individual consciousness) and internodal connections (ways of these elements’ interaction). Internodal connections can have different direction and diverse activation levels. The strongest connections are the semantic ones. They form semantic subnetworks that can be treated as analogues of semantic fields in mental lexicon. The paper tests the hypothesis that semantic fields (subnetworks) are interconnected by way of connections of their units. The method of directed chained associative test (with a chain of not less than 30 reactions) was used in the research. The research material is represented by reactions’ chains received from 139 informants (the total of 4334 reactions). The material was processed in the “Semograph” Information System (semograph.com) that gives a possibility to create semantic classification of reactions with many-to-many correspondence between reactions and fields. The sequence of activating semantic fields in reactions’ chains was analyzed. The received data confirm the hypothesis that mental lexicon units are grouped according to the field principle. In this context separate fields due to the connections of their units are more closely interconnected with each other than with all other fields. The activation of the fields’ connections has a directed character.

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Publication details

Title
MODELING CONNECTIONS OF SEMANTIC FIELDS IN MENTAL LEXICON
Authors
E.V. Erofeeva, K. I. Belousov, Y. E. Leshchenko
Proceedings
4th International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM 2017
Publisher
STEF92 Technology
Year
2017
Pages
841-848
SWS Citekey
Erofeeva201714841848
ISSN
2367-5659
ISBN
978-619-7408-19-5
Language
en
Publication type
Proceedings Paper
Keywords
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