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 Journal of Statistics and Management Systems cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

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Open Access Research Article

Enhancing word sense disambiguation through contextual embedding and optimization techniques

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pp. 337–347Vol. 27Issue 2March 2024DOI: 10.47974/JSMS-1259XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1259
Pages:
337–347

Abstract

This study presents a new strategy for determining the meaning of words based on their use in text. Traditional methods struggle because they do not fully grasp the context around ambiguous words. Our framework combines powerful models that understand context, like BERT and ELMO, to precisely identify a word’s sense based on its language environment. These representations depict word meanings in different situations more accurately. This helps the model to predict various meanings of an unclear word. Additionally, we apply techniques to refine the model’s performance. For instance, we customize the representations specifically for making sense of words. This increases the model’s awareness of subtle clues. Experiments show our approach notably outperforms basic methods in correctly determining word senses. The blending of contextual representations and refinement strategies not only boosts overall accuracy for defining ambiguous words but also demonstrates flexibility in handling difficult examples across diverse language contexts.

Keywords

Subject Classifications

Primary 93A30Secondary 49K15

References

[1] Y. Wang, M. Wang, Fine-grained opinion extraction from Chinese car reviews with an integrated strategy, J. Shanghai Jiaotong Univ. 23 (3) 1–7 (2018).
[2] R. Mihalcea, D.I. Moldovan, “Extended wordNet: progress report”, in: Proceedings of the North American Chapter of the Association for Computational Linguistics Workshop on WordNet and Other Lexical Resources, NAACL ‘01, pp. 95–100 (2001).
[3] F. Wang, W. Wu, Z. Li, M. Zhou, “Named entity disambiguation for questions in community question answering”, Knowl.-Based Syst. 126, 68–77 (2017).
[4] T. NiGuang, “The methods of word sense disambiguation,” 2011 International Conference on Electrical and Control Engineering, Yichang, China, pp. 3247-3249 (2011), doi: 10.1109/ICECENG.2011.6057474.
[5] C. -X. Zhang, Y. -L. Shao and X. -Y. Gao, “Word Sense Disambiguation Based on RegNet With Efficient Channel Attention and Dilated Convolution,” in IEEE Access, vol. 11, pp. 130733-130742 (2023), doi: 10.1109/ACCESS.2023.3335041.
[6] B. Scarlini, T. Pasini and R. Navigli, “With more contexts comes better performance: Contextualized sense embeddings for all-round word sense disambiguation”, Proc. Conf. Empirical Methods Natural Lang. Process. (EMNLP), pp. 3528-3539 (2020).
[7] Y. Kim, “Convolutional neural networks for sentence classification”, Proc. Conf. Empirical Methods Natural Lang. Process. (EMNLP), pp. 1746-1751, Oct. (2014).
[8] Y. Du, N. Holla, X. Zhen, C. Snoek and E. Shutova, “Meta-learning with variational semantic memory for word sense disambiguation”, Proc. 59th Annu. Meeting Assoc. Comput. Linguistics 11th Int. Joint Conf. Natural Lang. Process., vol. 1, pp. 5254-5268, Aug. (2021).
[9] Z. Wang, Y. Chan, J. Gao and J. Wu, “Word-based disambiguation based on neural network”, Software, vol. 40, no. 2, pp. 11-15 (2019).
[10] A. R. Pal, D. Saha, N. S. Dash, S. K. Naskar and A. Pal, “A novel approach to word sense disambiguation in Bengali language using supervised methodology”, Sādhanā, vol. 44, no. 8, pp. 181, Aug. (2019).
[11] H. Chen, M. Xia and D. Chen, “Non-parametric few-shot learning for word sense disambiguation”, Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics Human Lang. Technol., pp. 1774-1781, Jun. (2021).
[12] A. Koptient and N. Grabar, “Disambiguation of medical abbreviations in French with supervised methods”, Stud. Health Technol. Inform., vol. 281, pp. 313-317, May (2021).
[13] E. Barba, T. Pasini and R. Navigli, “ESC: Redesigning WSD with extractive sense comprehension”, Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics Human Lang. Technol., pp. 4661-4672, Jun. (2021).

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