Word Sense Disambiguation: A Supervised Semantic Similarity based Complex Network Approach


  • Chandrakant Kokane Savitribai Phule Pune University & SKN College of Engineering, Pune
  • Sachin Babar Savitribai Phule Pune University & Sinhgad Institute of Technology, Lonavala
  • Parikshit Mahalle Vishwakarma Institute of Information Technology, Pune
  • Shivprasad Patil Savitribai Phule Pune University & NBN Sinhgad School of Engineering, Pune


Lexical ambiguity, Semantic Similarity, Complex network, Sense disambiguation


Lexical ambiguity in machine translation and information retrieval is the challenge. Lexical ambiguity is caused by polysemous words where the word has multiple meanings. In Natural Language Processing before processing human commands the disambiguation of ambiguous commands should be done. The existing disambiguation methodologies disambiguate ambiguous sentences with available context information. The main identified problem is what if an ambiguous sentence doesn’t have enough information for disambiguation. The proposed model elaborates an adaptive sentence semantic similarity based complex network approach for identification of ambiguity and resolving it using semantic information. The discussed model represents the sentences of ambiguous documents as a vertex. The weighted complex network is constructed with respect to semantic similarities. The complex network is further processed for the ambiguous sentences having lack of context information. The main goal of this model is to provide an adaptive solution to lexical ambiguity of the paragraph or large document.


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B. S. Rintyarna and R. Sarno, "Adapted weighted graph for Word Sense Disambiguation," 2016 4th International Conference on Information and Communication Technology (ICoICT), 2016, pp. 1-5, DOI: 10.1109/ICoICT.2016.7571884.

Yadav, P. ., S. . Kumar, and D. K. J. . Saini. “A Novel Method of Butterfly Optimization Algorithm for Load Balancing in Cloud Computing”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 8, Aug. 2022, pp. 110-5, doi:10.17762/ijritcc.v10i8.5683.

C. D. Kokane, S. D. Babar and P. N. Mahalle, "Word Sense Disambiguation for Large Documents Using Neural Network Model," 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2021, pp. 1-5, DOI: 10.1109/ICCCNT51525.2021.9580101.

R. Navigli and M. Lapata, "An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 4, pp. 678-692, April 2010, DOI: 10.1109/TPAMI.2009.36.

Ghazaly, N. M. . (2022). Data Catalogue Approaches, Implementation and Adoption: A Study of Purpose of Data Catalogue. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(1), 01–04. https://doi.org/10.17762/ijfrcsce.v8i1.2063

Rada Mihalcea. 2005. Unsupervised large-vocabulary word sense disambiguation with graph-based algorithms for sequence data labelling. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing. Association for Computational Linguistics, USA, 411–418. https://DOI.org/10.3115/1220575.1220627

R. Rao and J. S. Kallimani, "Analysis of polysemy words in Kannada sentences based on parts of speech," 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016, pp. 500-504, DOI: 10.1109/ICACCI.2016.7732095.

N. A. Libre. (2021). A Discussion Platform for Enhancing Students Interaction in the Online Education. Journal of Online Engineering Education, 12(2), 07–12. Retrieved from http://onlineengineeringeducation.com/index.php/joee/article/view/49

F. Zait and N. Zarour, "Addressing Lexical and Semantic Ambiguity in Natural Language Requirements," 2018 Fifth International Symposium on Innovation in Information and Communication Technology (ISIICT), 2018, pp. 1-7, DOI: 10.1109/ISIICT.2018.8613726.

Rahman, Mohammad & Khan, Saeed & Hasan, K. M.. (2019). Word Sense Disambiguation by Context Detection. 1-6. 10.1109/EICT48899.2019.9068810.

A. M. Butnaru and R. T. Ionescu, "ShotgunWSD 2.0: An Improved Algorithm for Global Word Sense Disambiguation," in IEEE Access, vol. 7, pp. 120961-120975, 2019, DOI: 10.1109/ACCESS.2019.2938058.

Q. Nguyen, A. Vo, J. Shin and C. Ock, "Effect of Word Sense Disambiguation on Neural Machine Translation: A Case Study in Korean," in IEEE Access, vol. 6, pp. 38512-38523, 2018, DOI: 10.1109/ACCESS.2018.2851281.

Z. Li, F. Yang and Y. Luo, "Context Embedding Based on Bi-LSTM in Semi-Supervised Biomedical Word Sense Disambiguation," in IEEE Access, vol. 7, pp. 72928-72935, 2019, DOI: 10.1109/ACCESS.2019.2912584.

Kose, O., & Oktay, T. (2022). Hexarotor Yaw Flight Control with SPSA, PID Algorithm and Morphing. International Journal of Intelligent Systems and Applications in Engineering, 10(2), 216–221. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/1879

H. Calvo, A. P. Rocha-Ramírez, M. A. Moreno-Armendáriz and C. A. Duchanoy, "Toward Universal Word Sense Disambiguation Using Deep Neural Networks," in IEEE Access, vol. 7, pp. 60264-60275, 2019, DOI: 10.1109/ACCESS.2019.2914921.

Functional Blocks of Methodology




How to Cite

C. . Kokane, S. . Babar, P. . Mahalle, and S. . Patil, “Word Sense Disambiguation: A Supervised Semantic Similarity based Complex Network Approach”, Int J Intell Syst Appl Eng, vol. 10, no. 1s, pp. 90–94, Oct. 2022.