Modeling word perception using the Elman network

This paper presents an automatic acquisition process to acquire the semantic meaning for the words. This process obtains the representation vectors for stemmed words by iteratively improving the vectors, using a trained Elman network. Experiments performed on a corpus composed of Shakespeare's...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2008-10, Vol.71 (16), p.3150-3157
Main Authors: Liou, Cheng-Yuan, Huang, Jau-Chi, Yang, Wen-Chie
Format: Article
Language:English
Subjects:
Authorship
Categorization
Compositional representation
Content addressable memory
Elman network
Linguistic analysis
Personalized code
Polysemous word
Semantic search
Stylistic similarity
Word perception
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Summary:This paper presents an automatic acquisition process to acquire the semantic meaning for the words. This process obtains the representation vectors for stemmed words by iteratively improving the vectors, using a trained Elman network. Experiments performed on a corpus composed of Shakespeare's writings show its linguistic analysis and categorization abilities.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2008.04.030