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Communication Dans Un Congrès Année : 2021

A Novel Neurofuzzy Approach for Semantic Similarity Measurement

Résumé

The problem of identifying the degree of semantic similarity between two textual statements automatically has grown in importance in recent times. Its impact on various computer-related domains and recent breakthroughs in neural computation has increased the opportunities for better solutions to be developed. This research takes the research efforts a step further by designing and developing a novel neurofuzzy approach for semantic textual similarity that uses neural networks and fuzzy logics. The fundamental notion is to combine the remarkable capabilities of the current neural models for working with text with the possibilities that fuzzy logic provides for aggregating numerical information in a tailored manner. The results of our experiments suggest that this approach is capable of accurately determining semantic textual similarity.
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Dates et versions

hal-03481565 , version 1 (15-12-2021)

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Citer

Jorge Martinez-Gil, Riad Mokadem, Josef Küng, Abdelkader Hameurlain. A Novel Neurofuzzy Approach for Semantic Similarity Measurement. 23rd International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2021), Sep 2021, Virtual, France. pp.192-203, ⟨10.1007/978-3-030-86534-4_18⟩. ⟨hal-03481565⟩
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