Abstract
Finding semantic similarity between two words or concepts has been considered as a challenging task in the field of natural language processing. Some lexical ontology-based approaches have been developed for this purpose. However, these approaches have been tested only for English language. Based on survey, there is no computational model for calculating semantic similarity between Hindi concepts. We cannot ignore Hindi language, because it is the third most spoken language of the world. In this paper, we present a computational model for calculating semantic similarity between words/concepts with the help of lexical ontology, which has been tested for Hindi language. Further, experiments have been carried out on a benchmark data set translated from English to Hindi. In our proposed computational model, Hindi WordNet has been used to get relational information between Hindi concepts. Existing popular semantic similarity approaches have been used to calculate semantic similarity. Miller and Charles’s benchmark data set was used to evaluate our proposed approach. We calculated the semantic similarity between 20 word pairs by using three different semantic similarity measures. Accuracy of the results was measured by calculating correlation coefficient between these similarity measures and human judgment. Our proposed model is useful in following ways. Firstly, it allows us to study and analyze the results of available semantic similarity methods on Hindi words. Secondly, it provides a general module along with algorithms, which can be tuned to develop similar modules for any other language.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lin, Y., Bandar, J.A., Mclean, D.: An approach for measuring semantic similarity between words using multiple information sources. IEEE Trans. Knowl. Data Eng. 15(4), 871–882 (2003)
Wagh, K., Kohle, S.: Information retrieval based on semantic similarity using information content. Int. J. Comput. Sci. Issues 8(2/4), 364–370 (2011)
Gruber, T.R.: Towards principles for the design of ontologies used for knowledge sharing. Int. J. Hum. Comput. Stud. 43(5/6), 907–928 (1995)
Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)
Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. In: Fellbaum, C. (ed.) WordNet: An Electronic Lexical Database, pp. 265–283. MIT Press, Cambridge (1998)
Wu, Z., Palmer, M.: Verb semantic and lexical selection. In: Proceeding of the 32nd Annual Meeting of the Association for Computational Linguistics, pp. 133–138. Las Cruces, New Mexico (1994)
Mihalcea, R., Corley, C., Strapparava, C.: Corpus-Based and Knowledge-Based Measures of Text Semantic Similarity, pp. 775–781. American Association for Artificial Intelligence, Boston (2006)
Resnik, P.: Using information content to evaluate semantic similarity. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 448–453. Montreal (1999)
Bhattacharyya, P.: IndoWordNet. In: The 7th International Conference on Language Resources and Evaluation, Malta (2010)
Miller, G.A., Charles, W.G.: Contextual correlates of semantic similarity. Lang. Cogn. Process. 6, 1–28 (1991)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Singh, J., Sharan, A. (2014). Lexical Ontology-Based Computational Model to Find Semantic Similarity. In: Mohapatra, D.P., Patnaik, S. (eds) Intelligent Computing, Networking, and Informatics. Advances in Intelligent Systems and Computing, vol 243. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1665-0_12
Download citation
DOI: https://doi.org/10.1007/978-81-322-1665-0_12
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1664-3
Online ISBN: 978-81-322-1665-0
eBook Packages: EngineeringEngineering (R0)