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.
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References
Angelov, P.P., Buswell, R.A.: Automatic generation of fuzzy rule-based models from data by genetic algorithms. Inf. Sci. 150(1–2), 17–31 (2003)
Aouicha, M.B., Taieb, M.A.H., Hamadou, A.B.: LWCR: multi-layered Wikipedia representation for computing word relatedness. Neurocomputing 216, 816–843 (2016)
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguistics 5, 135–146 (2017)
Cer, D., et al.: Universal sentence encoder for English. In: Blanco, E., Lu, W. (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018: System Demonstrations, Brussels, Belgium, 31 October–4 November 2018, pp. 169–174. Association for Computational Linguistics (2018)
Cilibrasi, R., Vitányi, P.M.B.: The google similarity distance. IEEE Trans. Knowl. Data Eng. 19(3), 370–383 (2007)
Cingolani, P., Alcalá-Fdez, J.: jFuzzyLogic: a java library to design fuzzy logic controllers according to the standard for fuzzy control programming. Int. J. Comput. Intell. Syst. 6(sup1), 61–75 (2013)
Cordón, O.: A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: designing interpretable genetic fuzzy systems. Int. J. Approx. Reason. 52(6), 894–913 (2011)
Dai, B., Li, J., Xu, R.: Multiple positional self-attention network for text classification. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York , NY, USA, 7–12 February 2020, pp. 7610–7617. AAAI Press (2020)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Faruqui, M., Dyer, C.: Improving vector space word representations using multilingual correlation. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014, Gothenburg, Sweden, 26–30 April 2014, pp. 462–471 (2014)
Harispe, S., Ranwez, S., Janaqi, S., Montmain, J.: Semantic Similarity from Natural Language and Ontology Analysis. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers (2015)
Huang, E.H., Socher, R., Manning, C.D., Ng, A.Y.: Improving word representations via global context and multiple word prototypes. In: The 50th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, Jeju Island, Korea, 8–14 July 2012, Volume 1: Long Papers, pp. 873–882 (2012)
Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of the 10th Research on Computational Linguistics International Conference, ROCLING 1997, Taipei, Taiwan, August 1997, pp. 19–33 (1997)
Lastra-Díaz, J.J., García-Serrano, A., Batet, M., Fernández, M., Chirigati, F.: HESML: a scalable ontology-based semantic similarity measures library with a set of reproducible experiments and a replication dataset. Inf. Syst. 66, 97–118 (2017)
Lastra-Díaz, J.J., Goikoetxea, J., Taieb, M.A.H., García-Serrano, A., Aouicha, M.B., Agirre, E.: A reproducible survey on word embeddings and ontology-based methods for word similarity: linear combinations outperform the state of the art Eng. Appl. Artif. Intell. 85, 645–665 (2019)
Leacock, C., Chodorow, M.: Combining local context and wordnet similarity for word sense identification. WordNet Electron. Lexical Database 49(2), 265–283 (1998)
Li, Y., Bandar, Z., McLean, D.: An approach for measuring semantic similarity between words using multiple information sources. IEEE Trans. Knowl. Data Eng. 15(4), 871–882 (2003)
Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), Madison, Wisconsin, USA, 24–27 July 1998, pp. 296–304 (1998)
Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Hum.-Comput. Stud. 51(2), 135–147 (1999)
Martinez-Gil, J.: CoTO: a novel approach for fuzzy aggregation of semantic similarity measures. Cogn. Syst. Res. 40, 8–17 (2016)
Martinez-Gil, J.: Semantic similarity aggregators for very short textual expressions: a case study on landmarks and points of interest. J. Intell. Inf. Syst. 53(2), 361–380 (2019). https://doi.org/10.1007/s10844-019-00561-0
Martinez-Gil, J., Chaves-González, J.M.: Automatic design of semantic similarity controllers based on fuzzy logics. Expert Syst. Appl. 131, 45–59 (2019)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held 5–8 December 2013, Lake Tahoe, Nevada, United States, pp. 3111–3119 (2013)
Miller, G., Charles, W.: Contextual correlates of semantic similarity. Lang. Cogn. Process. 6(1), 1–28 (1991)
Peters, M.E., et al.: Deep contextualized word representations. In: Proceedings of NAACL-HLT, pp. 2227–2237 (2018)
Resnik, P.: Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J. Artif. Intell. Res. 11, 95–130 (1999)
Rutkowski, L., Cpalka, K.: Flexible neuro-fuzzy systems. IEEE Trans. Neural Networks 14(3), 554–574 (2003)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15(1), 116–132 (1985)
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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