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A Novel Neurofuzzy Approach for Semantic Similarity Measurement

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 12925)

Abstract

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.

Keywords

  • Data integration
  • Neurofuzzy
  • Semantic similarity
  • Deep learning applications

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Acknowledgements

This work has been supported by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry of Science, Research and Economy, and the State of Upper Austria in the frame of the COMET center SCCH. By the project FR06/2020 by International Cooperation & Mobility (ICM) of the Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH). We would also thank ‘the French Ministry of Foreign and European Affairs’ and ‘The French Ministry of Higher Education and Research’ which support the Amadeus program 2020 (French-Austrian Hubert Curien Partnership – PHC) Project Number 44086TD.

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Martinez-Gil, J., Mokadem, R., Küng, J., Hameurlain, A. (2021). A Novel Neurofuzzy Approach for Semantic Similarity Measurement. In: Golfarelli, M., Wrembel, R., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2021. Lecture Notes in Computer Science(), vol 12925. Springer, Cham. https://doi.org/10.1007/978-3-030-86534-4_18

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  • DOI: https://doi.org/10.1007/978-3-030-86534-4_18

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