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Article Dans Une Revue Data and Knowledge Engineering Année : 2023

Neurofuzzy semantic similarity measurement

Mesure de similarité sémantique

Résumé

Automatically identifying the degree of semantic similarity between two small pieces of text has grown in importance recently. Its impact on various computer-related domains and recent break-throughs in neural computation has increased the opportunities for better solutions to be developed. This work contributes a neurofuzzy approach for semantic textual similarity that uses neural networks and fuzzy logics. The idea is to combine the capabilities of the deep neural models for working with text with the ones from fuzzy logic for aggregating numerical data. The results of our experiments suggest that such an approach can accurately determine semantic similarity.
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Dates et versions

hal-04060914 , version 1 (06-04-2023)

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Jorge Martinez-Gil, Riad Mokadem, Josef Küng, Abdelkader Hameurlain. Neurofuzzy semantic similarity measurement. Data and Knowledge Engineering, 2023, 145, pp.102155. ⟨10.1016/j.datak.2023.102155⟩. ⟨hal-04060914⟩
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