NEFUSI: NeuroFuzzy Similarity. Final Report

Martinez-Gil, Jorge NEFUSI: NeuroFuzzy Similarity. Final Report., 2022 [Preprint]

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English abstract

This research work presents the final report for the NEFUSI project. In fact, we present here our research findings on building neurofuzzy models that automatically evaluate semantic textual similarity in an accurate and timely manner. We show that neural networks and fuzzy logic have different features that make them suitable for certain problems but unsuitable for others. Neural networks, on the one hand, are valuable tools for identifying patterns. However, they need to make it easier for people to comply with the decisions. On the other hand, interpretation is possible within fuzzy logic systems, but they cannot automatically derive the rules they use to make those decisions. These constraints served as the primary reason for developing a novel intelligent hybrid system, which combines two approaches to circumvent the individual effects of both limitations simultaneously.

Item type: Preprint
Keywords: Neurofuzzy systems, Semantic Textual Similarity
Subjects: I. Information treatment for information services > ID. Knowledge representation.
I. Information treatment for information services > IE. Data and metadata structures.
L. Information technology and library technology > LL. Automated language processing.
Depositing user: Dr Jorge Martinez-Gil
Date deposited: 22 Dec 2022 08:32
Last modified: 22 Dec 2022 08:32
URI: http://hdl.handle.net/10760/43794

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