State-of-the-art in neurofuzzy systems for semantic textual similarity

Martinez-Gil, Jorge State-of-the-art in neurofuzzy systems for semantic textual similarity., 2022 [Preprint]

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English abstract

In this work, we present our research for building novel models that allow for processing information of textual nature effectively and efficiently. We show that both neural networks and fuzzy logic have specific properties that make them suited for particular problems and not others. On the one hand, neural networks are appropriate for recognizing patterns. However, they do not facilitate compliance with the decisions. On the other hand, fuzzy logic systems are interpretable. However, they cannot automatically derive the rules they use to make those decisions. These limitations have been the central driving force behind creating a novel intelligent hybrid system, where two techniques are combined to overcome both limitations at the individual level.

Item type: Preprint
Keywords: Text data, Unstructured Data, Neurofuzzy systems, Deep Learning Applications
Subjects: L. Information technology and library technology > LL. Automated language processing.
Depositing user: Dr Jorge Martinez-Gil
Date deposited: 27 Jul 2022 07:24
Last modified: 27 Jul 2022 07:24
URI: http://hdl.handle.net/10760/43465

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