Abstract
A methodology for sub-symbolic semantic encoding of words is presented. The methodology uses the standard, semantically highly-structured WordNet lexical database and the SemiDiscrete matrix Decomposition to obtain a vector representation with low memory requirements in a semantic n-space. The application of the proposed algorithm over all the WordNet words would lead to a useful tool for the sub-symbolic processing of texts.
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Pilato, G., Vassallo, G., Gaglio, S. (2005). Wordnet and Semidiscrete Decomposition for Sub-Symbolic Representation of Words. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Biological and Artificial Intelligence Environments. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3432-6_23
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DOI: https://doi.org/10.1007/1-4020-3432-6_23
Publisher Name: Springer, Dordrecht
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