Nowadays, In Natural Language Processing (NLP), using artificial intelligence is a open challenge. Word Sense Disambiguation (WSD) is a sub field of artificial intelligence. In this research paper, WSD system is developed and validated for regional Telugu language. Many Natural Languages are having many ambiguous words. The word having more than one sense is known as ambiguous word or polysemy word. Word Sense Disambiguation is termed as the methodology of finding the appropriate sense of the ambiguity word. To develop WSD system a training dataset Lexical Knowledge Base LKB is used. In Query Expansion, WSD technique is used to increase the accuracy of the information retrieval system. In this research paper, baseline method and the proposed method Word First Sense are discussed. The evaluation metrics used are Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP) metrics. The results are encouraging in the proposed method.
- Query expansion
- Artificial intelligence
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Agirre, E., De Lacalle, O.L., Soroa, A.: Random walks for knowledge-based word sense disambiguation. Comput. Linguist. 40(1), 57–84 (2014)
Navigli, R.: Word sense disambiguation: A survey. ACM Comput. Surv. (CSUR) 41(2), 10 (2009)
Agirre, E., Soroa, A.: Categorized page rank for word sense disambiguation. In: The Proceedings of EACL-2009, Athens, Greece (2009)
Koppula, N., Padmaja Rani, B.: Word sense disambiguation using knowledge based approach in regional language. J. Adv. Res. Dyn. Control Syst. 5(special issue), 109–111 (2018)
Zoponal_Bayaty, B.F., Joshi, S.: Word sense disambiguation (WSD) and information retrieval (IR): literature review. Int. J. Adv. Res. Comput. Sci. Softw. Eng., 4(2) (2014). ISSN: 2277 128X
Ponzetto, S.P., Navigli, R.: Knowledge-rich word sense disambiguation rivaling supervised systems. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 1522–1531 (2010)
Mihalcea, R.: Knowledge Based Methods for WSD, e-ISBN 978-1-4020-4809-2. Springer (2007)
Navigli, R., Velardi, P.: Structural semantic interconnections: A knowledge-based approach to word sense disambiguation. IEEE Trans. Pattern Anal. Mach. Intell. 27(7), 1075–1086 (2005)
Neeraja, K., Padmaja Rani, B., Srinivas Rao, K.: Hybrid approaches for word sense disambiguation: a survey. Int. J. Appl. Eng. Res. 10(23), 43891–43895 (2015). ISSN 0973-4562
Ranjan Pal, A., Saha, D., Pal, A.: A knowledge based methodology for word sense disambiguation for low resource language. Adv. Comput. Sci. Technol. 10(2) 267–283 (2017). ISSN 0973-6107. © Research India Publications http://www.ripublication.com
Chatterjee, A., Joshii, S., Bhattacharyya, P., Kanojia, D., Meena, A.: A study of the sense annotation process: man v/s machine. In: International Conference on Global Wordnets. Matsue, Japan (2012)
Nameh, M.S., Fakhrahmad, M., Jahromi, M.Z.: A new approach to word sense disambiguation based on context similarity. In: Proceedings of the World Congress on Engineering, vol. I (2011)
I sincerely thank the principal and management of MLR Institute of Technology for providing me the facilities Laboratory and Internet WI-FI. I also thank all the authors in my reference section which helped me a lot in framing this research article.
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Koppula, N., Pradeep Kumar, J., Srinivas Rao, K., Kiran Kumar, G. (2022). Word Sense Disambiguation System for Information Retrieval in Telugu Language. In: Mandal, J.K., De, D. (eds) Advanced Techniques for IoT Applications. EAIT 2021. Lecture Notes in Networks and Systems, vol 292. Springer, Singapore. https://doi.org/10.1007/978-981-16-4435-1_23
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-4434-4
Online ISBN: 978-981-16-4435-1