Abstract
A new methodology for sub-symbolic semantic encoding of words is presented. The methodology uses the WordNet lexical database and an ad hoc modified Sammon algorithm to associate a vector to each word in a semantic n-space. All words have been grouped according to the WordNet lexicographers’ files classification criteria: these groups have been called lexical sets. The word vector is composed by two parts: the first one, takes into account the belonging of the word to one of these lexical sets; the second one is related to the meaning of the word and it is responsible for distinguishing the word among the other ones of the same lexical set. The application of the proposed technique over all the words of WordNet would lead to an interesting instrument for the sub-symbolic processing of texts. The first experimental results show the effectiveness of the proposed approach.
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Vassallo, G., Pilato, G., Maggio, A., Puglisi, A., Gaglio, S. (2003). Sub-symbolic Encoding of Words. In: Cappelli, A., Turini, F. (eds) AI*IA 2003: Advances in Artificial Intelligence. AI*IA 2003. Lecture Notes in Computer Science(), vol 2829. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39853-0_37
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DOI: https://doi.org/10.1007/978-3-540-39853-0_37
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