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State of the Art Analysis of Word Sense Disambiguation

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Intelligent Computing for Sustainable Development (ICICSD 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2122))

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Abstract

Word Sense Disambiguation (WSD) is a computational activity in natural language processing (NLP) that aims to determine the meaning of a word in its context by choosing the most appropriate meaning or definition from a predefined set of possibilities. WSD is essential for many applications such as machine translation, information retrieval, text classification, question answering, summarization, sentiment analysis, and word processing. WSD is a difficult task, especially in cases where words have multiple meanings or the context is ambiguous, which can lead to difficulties in choosing the correct meaning of a word. The WSD has applied many strategies to different datasets and corpora. This study used a knowledge-based approach and machine learning techniques to classify WSD algorithms. Each category is examined in-depth, along with an explanation of its associated algorithms. The paper reviews various approaches, and resources, used in WSD. The survey includes papers from various journals and discusses recent trends and competitions in the field, as well as future directions for research.

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References

  1. Yarowsky, D: Unsupervised word sense disambiguation rivaling supervised methods. In: Proceedings of ACL 1995, pp 189–196, Cambridge, Massachusetts (1995)

    Google Scholar 

  2. Agirre, E., Rigau, G., Padro, L., Atserias, J.: Combining supervised and unsupervised lexical knowledge methods for word sense disambiguation. Comput. Humanit. 34, 103–108 (2000)

    Article  Google Scholar 

  3. Banerjee, S., Pedersen, T.: An adapted Lesk algorithm for word sense disambiguation using WordNet. In: Computational Linguistics and Intelligent Text Processing. LNCS, vol. 2276, pp. 136–145. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45715-1_11

  4. Montoyo, A., Rigau, G., Suárez, A., Palomar, M.: Combining knowledge- and corpus-based word-sense-disambiguation methods. J. Artif. Intell. Res. 23, 299–330 (2005)

    Article  Google Scholar 

  5. Martinez, D., Agirre, E., Wang, X.: Word relatives in context for word sense disambiguation. In: Proceedings of the 2006 Australasian Language Technology Workshop (ALTW2006), pp. 42–50 (2006)

    Google Scholar 

  6. Mihalcea, R.: Using Wikipedia for automatic word sense disambiguation. In: Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference, pp. 196–203. Association for Computational Linguistics, Rochester (2007)

    Google Scholar 

  7. Khapra, M.M., Shah, S., Kedia, P., Bhattacharyya, P.: Projecting parameters for multilingual word sense disambiguation. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp. 459–467. Association for Computational Linguistics, Singapore (2009)

    Google Scholar 

  8. Sun, Y., Jia, K.-L.: Research of Word Sense Disambiguation Based on Mining Association Rule, pp. 86–88. Third International Symposium on Intelligent Information Technology Application Workshops, Nanchang (2009)

    Google Scholar 

  9. Banea, C., Mihalcea, R., Wiebe, J.: Sense-level subjectivity in a multilingual setting. In: Proceedings of the workshop on sentiment analysis where AI meets psychology (SAAIP 2011), pp. 44–50. Asian Federation of Natural Language Processing. Chiang Mai (2011)

    Google Scholar 

  10. Fernandez-Ordoñez, E., Mihalcea, R., Hassan, S.: Unsupervised word sense disambiguation with multilingual representations. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC 2012), pp. 847–851. European Language Resources Association (ELRA), Istanbul (2012)

    Google Scholar 

  11. Mishra, N., Siddiqui, T.J.: An investigation to semi supervised approach for HINDI word sense disambiguation. In: Trends in Innovative Computing Intelligent Systems Design (2012)

    Google Scholar 

  12. Kumari, S., Singh, P.: Optimized word sense disambiguation in Hindi using genetic algorithm. Int. J. Res. Comput. Commun. Technol. 2(7), 445–449 (2013)

    Google Scholar 

  13. Gutiérrez, Y., et al.: UMCC_DLSI: Reinforcing a Ranking Algorithm with Sense Frequencies and Multidimensional Semantic Resources to solve Multilingual Word Sense Disambiguation (2013)

    Google Scholar 

  14. Bala, P.: Word sense disambiguation using selectional restriction. Int. J. Sci. Res. Publ. 3(4) (2013)

    Google Scholar 

  15. Zapirain, B., Agirre, E., Marquez, L., Surdeanu, M.: Selectional preferences for semantic role classification. Comput. Linguist. 39, 631–663 (2013)

    Article  Google Scholar 

  16. Bhatt, B., Kunnath, S., Bhattacharyya, P.: Graph based algorithm for automatic domain segmentation of WordNet. In: Proceedings of the Seventh Global Wordnet Conference, pp. 178–185. University of Tartu Press, Tartu (2014)

    Google Scholar 

  17. Dhungana, U.R., Shakya, S., Baral, K., Sharma, B.: Word sense disambiguation using WSD specific WordNet of polysemy words. In: Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), Anaheim, 2015, pp. 148–152 (2015)

    Google Scholar 

  18. Yuan, D., Richardson, J., Doherty, R., Evans, C., Altendorf, E.: Semi-supervised word sense disambiguation with neural models. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers (COLING 2016), Osaka, pp. 1374–1385. The COLING 2016 Organizing Committee (2016)

    Google Scholar 

  19. Kågebäck, M., Salomonsson, H.: Word Sense Disambiguation Using a Bidirectional LSTM. CogALex@COLING (2016)

    Google Scholar 

  20. Abualhaija, S., Zimmermann, K.-H.: Solving specific domain word sense disambiguation using the D-Bees algorithm. Glob. J. Technol. Optimiz. (2016)

    Google Scholar 

  21. Chaplot, D.S., Salakhutdinov, R.: Knowledge-based word sense disambiguation using topic models. In: Proceedings of the AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  22. Kuila, A., Das, A., Sarkar, S.: A graph convolution network-based system for technical domain identification. In: Proceedings of the 17th International Conference on Natural Language Processing (ICON): TechDOfication 2020 Shared Task (2020)

    Google Scholar 

  23. Pawar, S., Thombre, S., Mittal, A., Ponkiya, G., Bhattacharyya, P.: Tap** BERT for Preposition Sense Disambiguation (2021)

    Google Scholar 

  24. Chen, H., **a, M., Chen, D.: Non-parametric few-shot learning for word sense disambiguation. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)

    Google Scholar 

  25. Du, Y., Holla, N., Zhen, X., Snoek, C.G.M., Shutova, E.: Meta-learning with variational semantic memory for word sense disambiguation. In: Annual Meeting of the Association for Computational Linguistic (2021)

    Google Scholar 

  26. Zobaed, S., Haque, M.E., Rabby, M.F., Salehi, M.A.: SensPick: sense picking for word sense disambiguation. In: Proceedings of 2021 IEEE 15th International Conference on Semantic Computing (ICSC) (2021)

    Google Scholar 

  27. Park, J.Y., Shin, H.J., Lee, J.S.: Word sense disambiguation using clustered sense labels. Appl. Sci. 12(4), 1857 (2022)

    Google Scholar 

  28. Su, Y., Zhang, H., Song, Y., Zhang, T.: Rare and zero-shot word sense disambiguation using Z-reweighting. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, Dublin, pp. 4713–4723 (2022)

    Google Scholar 

  29. Zhang, C.-X., Pang, S.-Y., Gao, X.-Y., Jia-Qi, L., Bo, Y.: Attention neural network for biomedical word sense disambiguation. Discrete Dyn. Nat. Soc. Hindawi 2022, 1–14 (2022)

    Google Scholar 

  30. Bhatia, S., Kumar, A., Khan, M.M.: Role of genetic algorithm in optimization of Hindi word sense disambiguation. IEEE Access 10, 75693–75707 (2022)

    Article  Google Scholar 

  31. Patankar, S., Phadke, M., Devane, S.: Wiki sense bag creation using multilingual word sense disambiguation. IAES Int. J. Artif. Intell. 11(1), 319–326 (2022)

    Google Scholar 

  32. Campolungo, N., Martelli, F., Saina, F., Navigli, R.: DiBiMT: a novel benchmark for measuring word sense disambiguation biases in machine translation. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 4331–4352 (2022)

    Google Scholar 

  33. Maru, M., Conia, S., Bevilacqua, M., Navigli, R.: Nibbling at the hard core of word sense disambiguation. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 4724–4737 (2022)

    Google Scholar 

  34. George, T., Vazirgiannis, M., Androutsopoulos, I.: Word sense disambiguation with spreading activation networks generated from Thesauri. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 1725–1730 (2007)

    Google Scholar 

  35. Samsuddoha, M., Biswas, D., Erfan, M.: User Similarity Computation Strategy for Collaborative Filtering Using Word Sense Disambiguation Technique, pp. 87–101. Springer, Singapore (2023). https://doi.org/10.1007/978-981-19-8032-9_7

  36. Ramya, P., Karthik, B.: Word sense disambiguation based sentiment classification using linear kernel learning scheme. Intell. Automat. Soft Comput. (2022)

    Google Scholar 

  37. Okpala, I., Rodriguez, G.R., Tapia, A., Halse, S., Kropczynski, J.: A semantic approach to negation detection and word disambiguation with natural language processing. In: Proceedings of the 6th International Conference on Natural Language Processing and Information Retrieval (NLPIR 2022) (2022)

    Google Scholar 

  38. Weaver, W.: MT News International, no. 22, July 1999, pp. 5–6, 15 (1949)

    Google Scholar 

  39. Edoardo, B., Pasini, T., Navigli, R.: ESC: redesigning WSD with extractive sense comprehension. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4661–4672 (2021)

    Google Scholar 

  40. Pal, A.R., Saha, D.: Word sense disambiguation: a survey. Int. J. Control Theory Comput. Model. 5(3), 1–6 (2015)

    Google Scholar 

  41. Aliwy, A.H., Taher, H.A.: Word sense disambiguation: survey study. J. Comput. Sci. 15(7), 1004–1011 (2019)

    Google Scholar 

  42. Sharma, P., Joshi, N.: Knowledge-based method for word sense disambiguation by using Hindi WordNet. Eng. Technol. Appl. Sci. Res. 9(2), 3985–3989 (2019)

    Google Scholar 

  43. Agirre, E., de Lacalle, O.L., Soroa, A.: The risk of sub-optimal use of open source NLP software: UKB is inadvertently state-of-the-art in knowledge-based WSD. In: Proceedings of Workshop for NLP Open Source Software, Melbourne, 20 July 2018, pp. 29–33 (2018)

    Google Scholar 

  44. Agirre, E., de Lacalle, O.L., Soroa, A.: Knowledge-based WSD on specific domains: performing better than generic supervised WSD. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI’09), pp. 1501–1506 (2009)

    Google Scholar 

  45. Tang, X., Chen, X., Qu, W., Yu, S.: Semi-supervised WSD in selectional preferences with semantic redundancy. Coling 2010: Poster Volume, pp. 1238–1246 (2010)

    Google Scholar 

  46. Başkaya, O., Jurgens, D.: Semi-supervised learning with induced word senses forstate of the art word sense disambiguation. J. Artif. Intell. Res. 55, 1025–1058 (2016)

    Google Scholar 

  47. Duarte, J.M., Sousa, S., Milios, E., Berton, L: Deep analysis of word sense disambiguation via semi-supervised learning and neural word representations. Inf. Sci. 570, 278–297 (2021). https://doi.org/10.1016/j.ins.2021.04.006

  48. Rani, P., Pudi, V., Sharma, D.M.: Semisupervised data driven word sense disambiguation for resource-poor languages. In: 4th International Conference on Natural Language Processing (ICON 2017) (2017)

    Google Scholar 

  49. Sousa, S., Milios, E., Berton, L.: Word sense disambiguation: an evaluation study of semi-supervised approaches with word embeddings. In: 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, pp. 1–8 (2020). https://doi.org/10.1109/IJCNN48605.2020.9207225

  50. Taghipour, K., Ng, H.: Semi-Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains, pp. 314–323 (2015). https://doi.org/10.3115/v1/N15-1035

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Authors and Affiliations

  1. Vishwakarma Institute of Information Technology, Pune, India

    Madhuri Karnik, Vaishali Mishra, Vidya Gaikwad, Disha Wankhede, Priya Chougale, Vaishnavi Pophale, Arpit Zope & Chirag Maski

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  1. Madhuri Karnik
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  2. Vaishali Mishra
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  3. Vidya Gaikwad
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  4. Disha Wankhede
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  5. Priya Chougale
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  6. Vaishnavi Pophale
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  7. Arpit Zope
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  8. Chirag Maski
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Corresponding author

Correspondence to Vaishnavi Pophale .

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Editors and Affiliations

  1. Anurag University, Hyderabad, Telangana, India

    S. Satheeskumaran

  2. University of Leicester, Leicester, UK

    Yudong Zhang

  3. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-pei Hong

  5. University of Teramo, Teramo, Italy

    Danilo Pelusi

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Karnik, M. et al. (2024). State of the Art Analysis of Word Sense Disambiguation. In: Satheeskumaran, S., Zhang, Y., Balas, V.E., Hong, Tp., Pelusi, D. (eds) Intelligent Computing for Sustainable Development. ICICSD 2023. Communications in Computer and Information Science, vol 2122. Springer, Cham. https://doi.org/10.1007/978-3-031-61298-5_5

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  • DOI: https://doi.org/10.1007/978-3-031-61298-5_5

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