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Implementing WordNet Measures of Lexical Semantic Similarity in a Fuzzy Logic Programming System

Published online by Cambridge University Press:  03 March 2021

PASCUAL JULIÁN-IRANZO
Affiliation:
Department of Information Technologies and Systems, University of Castilla-La Mancha, 13071Ciudad Real, Spain (e-mail: [email protected])
FERNANDO SÁENZ-PÉREZ
Affiliation:
Faculty of Computer Science, Complutense University of Madrid, 28040Madrid, Spain (e-mail: [email protected])

Abstract

This paper introduces techniques to integrate WordNet into a Fuzzy Logic Programming system. Since WordNet relates words but does not give graded information on the relation between them, we have implemented standard similarity measures and new directives allowing the proximity equations linking two words to be generated with an approximation degree. Proximity equations are the key syntactic structures which, in addition to a weak unification algorithm, make a flexible query-answering process possible in this kind of programming language. This addition widens the scope of Fuzzy Logic Programming, allowing certain forms of lexical reasoning, and reinforcing Natural Language Processing (NLP) applications.

Type
Technical Note
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

*

Work is partially funded by the State Research Agency (AEI) of the Spanish Ministry of Science and Innovation under grant PID2019-104735RB-C42 (SAFER), by the Spanish Ministry of Economy and Competitiveness, under the grants TIN2016-76843-C4-2-R (MERINET), TIN2017-86217-R (CAVI-ART-2), and by the Comunidad de Madrid, under the grant S2018/TCS-4339 (BLOQUES-CM), co-funded by EIE Funds of the European Union.

References

Al-Sayadi, S. H., Julián-Iranzo, P., Romero, F. P. and Sáenz-Pérez, F. 2020. A fuzzy declarative approach to classify unlabeled short texts based on automatically constructed WordNet ontologies. In Proceedings of the 12th European Symposium on Computational Intelligence and Mathematics, ESCIM 2020. 16.Google Scholar
Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E., Gutierrez, J. and Kochut, K. 2017. A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques. CoRR abs/1707.02919.Google Scholar
Baeza-Yates, R. and Ribeiro-Neto, B. 2011. Modern Information Retrieval – The Concepts and Technology Behind Search, 1st ed. Pearson Education Ltd., Harlow, England.Google Scholar
Budanitsky, A. and Hirst, G. 2006. Evaluating WordNet-based measures of lexical semantic relatedness. Computational Linguistics 32, 1, 1347.CrossRefGoogle Scholar
Çakir, E. and Ulukan, H. Z. 2019. A fuzzy logic programming environment for recycling facility selection. In Proceedings of the 11th International Joint Conference on Computational Intelligence, IJCCI 2019. ScitePress, 367374.Google Scholar
Çakir, E. and Ulukan, H. Z. 2020. A fuzzy linguistic programming for sustainable ecotourism activities. In Proceedings of the 10th Annual Computing and Communication Workshop and Conference, CCWC 2020. IEEE, 121126.Google Scholar
Fellbaum, C. 1998. WordNet: An Electronic Lexical Database. MIT Press.CrossRefGoogle Scholar
Fellbaum, C. 2006. WordNet(s). In Encyclopedia of Language & Linguistics, 2nd ed., K. B. E. in Chief), Ed. Vol. 13. Elsevier, Oxford, 665670.Google Scholar
Fellbaum, C. et al. 2006. WordNet File Formats: prologdb(5WN). https://wordnet.princeton.edu/documentation/prologdb5wn Google Scholar
Fontana, F. A. and Formato, F. 1999. Likelog: A logic programming language for flexible data retrieval. In Proceedings of the 1999 ACM Symposium on Applied Computing (SAC’99), 260267.Google Scholar
Fontana, F. A. and Formato, F. 2002. A Similarity-based Resolution Rule. International Journal of Intelligent Systems 17, 9, 853872.CrossRefGoogle Scholar
Francis, W. N. and Kucera, H. 1964, 1971, 1979. A Standard Corpus of Present-Day Edited American English, for use with Digital Computers (Brown). https://www.sketchengine.eu/brown-corpus/.Google Scholar
Jiang, J. J. and Conrath, D. W. 1997. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of the 10th Research on Computational Linguistics International Conference, ROCLING 1997. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 1933.Google Scholar
Julián-Iranzo, P. and Rubio-Manzano, C. 2015. Proximity-based unification theory. Fuzzy Sets and Systems 262, 2143.Google Scholar
Julián-Iranzo, P. and Rubio-Manzano, C. 2017. A sound and complete semantics for a similarity-based logic programming language. Fuzzy Sets and Systems 317, 126.CrossRefGoogle Scholar
Julián-Iranzo, P. and Sáenz-Pérez, F. 2017. FuzzyDES or how DES met Bousi Prolog. In Proceedings of the 2017 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2017, 16.Google Scholar
Julián-Iranzo, P. and Sáenz-Pérez, F. 2018a. An efficient proximity-based unification algorithm. In Proceedings of the 2018 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2018, 18.Google Scholar
Julián-Iranzo, P. and Sáenz-Pérez, F. 2018b. A fuzzy datalog deductive database system. IEEE Transactions on Fuzzy Systems 26, 26342648.CrossRefGoogle Scholar
Julián-Iranzo, P. and Sáenz-Pérez, F. 2019. WordNet and Prolog: why not? In Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019, 18.Google Scholar
Leacock, C. and Chodorow, M. 1998. Combining local context and Wordnet similarity for word sense identification. In WordNet: An Electronic Lexical Database, C. Fellbaum, Ed. MIT Press, 265283.Google Scholar
Lee, R. 1972. Fuzzy logic and the resolution principle. Journal of the ACM 19, 1, 119129.Google Scholar
Lin, D. 1998. An information-theoretic definition of similarity. In Proceedings of the 15th International Conference on Machine Learning. Morgan Kaufmann, 296304.Google Scholar
Loia, V., Senatore, S. and Sessa, M. I. 2001. Similarity-based SLD resolution and its implementation in an extended prolog system. In Proceedings of the 2001 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2001, 650653.Google Scholar
Martelli, A. and Montanari, U. 1982. An efficient unification algorithm. ACM Transactions on Programming Languages and Systems 4, 258282.CrossRefGoogle Scholar
Miller, G. A. 1995. WordNet: a lexical database for English. Communications of the ACM 38, 11, 3941.CrossRefGoogle Scholar
Miller, G. A., Leacock, C., Tengi, R. and Bunker, R. 1993. A semantic concordance. In Proceedings of the Workshop on Human Language Technology, HLT 1993, 303–308.Google Scholar
Pedersen, T., Patwardhan, S. and Michelizzi, J. 2004. WordNet:similarity – measuring the relatedness of concepts. In Proceedings of the Nineteenth National Conference on Artificial Intelligence, Sixteenth Conference on Innovative Applications of Artificial Intelligence. AAAI Press/The MIT Press, 10241025.Google Scholar
Resnik, P. 1995. Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI 95, 2 Volumes. Morgan Kaufmann, 448453.Google Scholar
Romero, F. P., Julián-Iranzo, P., Ferreira-Satler, M. and Gallardo-Casero, J. 2013. Classifying unlabeled short texts using a fuzzy declarative approach. Language Resources and Evaluation 47, 1, 151178.CrossRefGoogle Scholar
Rubio-Manzano, C. and Julián-Iranzo, P. 2014. Fuzzy linguistic Prolog and its applications. Journal of Intelligent and Fuzzy Systems 26, 15031516.CrossRefGoogle Scholar
Rubio-Manzano, C. and Julián-Iranzo, P. 2015. Incorporation of abstraction capability in a logic-based framework by using proximity relations. Journal of Intelligent and Fuzzy Systems 29, 4, 16711683.CrossRefGoogle Scholar
Rubio-Manzano, C. and Triviño, G. 2016. Improving player experience in computer games by using players’ behavior analysis and linguistic descriptions. International Journal of Human-Computer Studies 95, 2738.CrossRefGoogle Scholar
Santus, E., Wang, H., Chersoni, E. and Zhang, Y. 2018. A rank-based similarity metric for word embeddings. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics ACL 2018 (2). Association for Computational Linguistics, 552557.Google Scholar
Serrano-Guerrero, J., Olivas, J. A., Romero, F. P. and Herrera-Viedma, E. 2015. Sentiment analysis: a review and comparative analysis of web services. Information Sciences 311, 1838.CrossRefGoogle Scholar
Sessa, M. I. 2002. Approximate reasoning by similarity-based SLD resolution. Theoretical Computer Science 275, 1–2, 389426.CrossRefGoogle Scholar
van Emden, M. and Kowalski, R. 1976. The semantics of predicate logic as a programming language. Journal of the ACM 23, 4, 733742.CrossRefGoogle Scholar
Wu, Z. and Palmer, M. S. 1994. Verb semantics and lexical selection. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics. Morgan Kaufmann Publishers/ACL, 133138.Google Scholar
Zadeh, L. 1965. Fuzzy sets. Information and Control 8, 338353.CrossRefGoogle Scholar