Abstract
Anthropocentrism of computational systems is totally justified when the task concerns to natural language. Computational linguistics systems use to rely on mathematical and statistical formalisms, which are efficient and useful but far from human procedures and therefore not so skilled. The presented work proposes a computational model of natural language reading, called Cognitive Reading Indexing Model (CRIM), inspired by some aspects of human cognition, trying to become as psychologically plausible as possible. The model relies on a semantic neural network and it produces not vectors but nets of activated concepts as text representations. The experimental evaluation shows that the system is suitable for real applications and also to model human reading, and it provides a framework to validate hypothesis from other Cognitive Science fields.
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Serrano, J.I., Iglesias, Á., del Castillo, M.D. (2007). A Connectionist Model of Human Reading. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_134
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DOI: https://doi.org/10.1007/978-3-540-73007-1_134
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