Modeling word perception using the Elman network
Resource
Neurocomputing 71 (16-18): 3150-3157
Journal
Neurocomputing
Journal Volume
71
Journal Issue
16-18
Pages
3150-3157
Date Issued
2008
Author(s)
Abstract
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. © 2008 Elsevier B.V. All rights reserved.
Subjects
Authorship; Categorization; Compositional representation; Content addressable memory; Elman network; Linguistic analysis; Personalized code; Polysemous word; Semantic search; Stylistic similarity; Word perception
SDGs
Other Subjects
Data storage equipment; Information theory; Linguistics; Semantics; Vectors; Authorship; Categorization; Compositional representation; Content addressable memory; Elman network; Linguistic analysis; Personalized code; Polysemous word; Semantic search; Stylistic similarity; Word perception; Neural networks; conference paper; learning algorithm; linguistics; literature; mathematical model; nerve cell network; priority journal; semantics; verbal memory; word recognition
Type
conference paper
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