Abstract
Due to the ever-evolving nature of human languages, the ambiguity in it needs to be dealt with by the researchers. Word sense disambiguation (WSD) is a classical problem of natural language processing which refers to identifying the most appropriate sense of a given word in the concerned context. WordNet graph based approaches are used by several state-of-art methods for performing WSD. This paper highlights a novel genetic algorithm based approach for performing WSD using fuzzy WordNet graph based approach. The fitness function is calculated using the fuzzy global measures of graph connectivity. For proposing this fitness function, a comparative study is performed for the global measures edge density, entropy and compactness. Also, an analytical insight is provided by presenting a visualization of the control terms for word sense disambiguation in the research papers from 2013 to 2018 present in Web of Science.
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References
Vij, S., Jain, A., Tayal, D., Castillo, O.: Fuzzy logic for inculcating significance of semantic relations in word sense disambiguation using a WordNet graph. Int. J. Fuzzy Syst. (2017). (Springer) https://doi.org/10.1007/s40815-017-0433-8
Jain, A., Lobiyal, D.K.: Fuzzy Hindi WordNet and word sense disambiguation using fuzzy graph connectivity measures. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 15(2), 8 (2016)
Navigli, R., Lapata, M.: An experimental study of graph connectivity for unsupervised word sense disambiguation. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 678–692 (2010)
Hung, J.C., Wang, C.S., Yang, C.Y., Chiu, M.S., Yee, G.: Applying word sense disambiguation to question answering system for e-learning. In: 19th International Conference on Advanced Information Networking and Applications, AINA 2005. vol. 1, pp. 157–162. IEEE, March 2005
Duque, A., Martinez-Romo, J., Araujo, L.: Can multilinguality improve biomedical word sense disambiguation? J. Biomed. Inf. 64, 320 (2016). (Academic Press Inc Elsevier Science) https://doi.org/10.1016/j.jbi.2016.10.020
Ellenius, J., Bergvall, T., Dasgupta, N., Hedfors, S., Pierce, C., Noren, G.N.: Medication name entity recognition in tweets using global dictionary lookup and word sense disambiguation. Pharmacoepidemiol. Drug Safety 25, 414 (2016) (Wiley-Blackwell)
Carpuat, M., Wu, D.: Improving statistical machine translation using word sense disambiguation. In: Emnlp-Conll, vol. 7, pp. 61–72, June 2007
Jain, A., Mittal, K., Tayal, D.K.: Automatically incorporating context meaning for query expansion using graph connectivity measures. Prog. Artif. Intell. 2(2–3), 129–139 (2014)
Moon, S., McInnes, B., Melton, G.B.: Challenges and practical approaches with word sense disambiguation of acronyms and abbreviations in the clinical domain. Healthcare Inform. Res. 21(1), 35 (2015). (Korean Soc Medical Informatics) https://doi.org/10.4258/hir.2015.21.1.35
Chasin, R., Rumshisky, A., Uzuner, O., Szolovitsl, P.: Word sense disambiguation in the clinical domain: a comparison of knowledge-rich and knowledge-poor unsupervised methods. J. Am. Med. Inform. Assoc. 21(5), 842 (2014). (BMJ Publishing Group) https://doi.org/10.1136/amiajnl-2013-002133
Chen, Y.K., Cao, H.X., Mei, Q.Z., Zheng, K., Xu, H.: Applying active learning to supervised word sense disambiguation in MEDLINE. J. Am. Med. Inform. Assoc. 20(5), 1001 (2013). (BMJ Publishing Group) https://doi.org/10.1136/amiajnl-2012-001244
Alsaeedan, W., Menai, M.E., Al-Ahmadi, S.: A hybrid genetic-ant colony optimization algorithm for the word sense disambiguation problem. Inf. Sci. 417, 20 (2017). (Elsevier Science Inc) https://doi.org/10.1016/j.ins.2017.07.002
Abualhaija, S., Zimmermann, K.H.: D-Bees: a novel method inspired by bee colony optimization for solving word sense disambiguation. Swarm Evol. Comput. 27, 188 (2016). (Elsevier Science Bv) https://doi.org/10.1016/j.swevo.2015.12.002
Abed, S.A., Tiun, S., Omar, N.: Word sense disambiguation in evolutionary manner. Connection Sci. 28(3), 226 (2016). (Taylor & Francis Ltd) https://doi.org/10.1080/09540091.2016.1141874
AlSaidi, B.K.: Automatic approach for word sense disambiguation using genetic algorithms. Int. J. Adv. Comput. Sci. Appl. 7(1), 41 (2016) (Science & Information Sai Organization Ltd)
Abed, S.A., Tiun, S., Omar, N.: Harmony search algorithm for word sense disambiguation. PLOS ONE 10(9) (2015). (Public Library Science) https://doi.org/10.1371/journal.pone.0136614
Menai, M.E.B.: Word sense disambiguation using evolutionary algorithms—application to Arabic language. Comput. Human Behav. 41, 92 (2014). (Pergamon-Elsevier Science Ltd) https://doi.org/10.1016/j.chb.2014.06.021
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Vij, S., Jain, A., Tayal, D. (2020). A Genetic Algorithm Based Approach for Word Sense Disambiguation Using Fuzzy WordNet Graphs. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_47
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DOI: https://doi.org/10.1007/978-3-030-35445-9_47
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