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 writings show its linguistic analysis and categorization abilities.
word perception elman network linguistic analysis categorization compositional representation authorship stylistic similarity semantic search personalized code content addressable memory polysemous word