A Literature Survey on Word Sense Disambiguation for the Hindi Language-Reference-Cited by-同舟云学术

A Literature Survey on Word Sense Disambiguation for the Hindi Language

Published:2023-09-07 Issue:9 Volume:14 Page:495
ISSN:2078-2489
Container-title:Information
language:en
Short-container-title:Information

Author:

Gujjar Vinto¹,Mago Neeru²,Kumari Raj³,Patel Shrikant⁴,Chintalapudi Nalini⁵^ORCID,Battineni Gopi⁵⁶^ORCID

Affiliation:

1. Department of Computer Science & Applications, Panjab University, Chandigarh 160014, India

2. Department of Computer Science & Applications, Panjab University Swami Sarvanand Giri Regional Centre, Hoshiarpur 160014, India

3. University Institute of Engineering and Technology, Panjab University, Chandigarh 160014, India

4. School of IT & ITES, Delhi Skill and Entrepreneurship University, Government of NCT of Delhi, Delhi 110003, India

5. Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy

6. Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddharth Engineering College, Vijayawada 520007, India

Abstract

Word sense disambiguation (WSD) is a process used to determine the most appropriate meaning of a word in a given contextual framework, particularly when the word is ambiguous. While WSD has been extensively studied for English, it remains a challenging problem for resource-scarce languages such as Hindi. Therefore, it is crucial to address ambiguity in Hindi to effectively and efficiently utilize it on the web for various applications such as machine translation, information retrieval, etc. The rich linguistic structure of Hindi, characterized by complex morphological variations and syntactic nuances, presents unique challenges in accurately determining the intended sense of a word within a given context. This review paper presents an overview of different approaches employed to resolve the ambiguity of Hindi words, including supervised, unsupervised, and knowledge-based methods. Additionally, the paper discusses applications, identifies open problems, presents conclusions, and suggests future research directions.

Publisher

MDPI AG

Subject

Information Systems

Link

https://www.mdpi.com/2078-2489/14/9/495/pdf

Reference73 articles.

1. Zipf, G.K. (1949). Human Behavior and the Principle of Least Effort, Addison-Wesley Press.

2. The preference semantics family;Wilks;Comput. Math. Appl.,1992

3. Word sense disambiguation: A survey;Navigli;ACM Comput. Surv.,2009

4. Vickrey, D., Biewald, L., Teyssier, M., and Koller, D. (2005, January 6). Word-sense disambiguation for machine translation. Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT/EMNLP 2005), Vancouver, BC, Canada.

5. Carpuat, M., and Wu, D. (2007, January 28–30). Improving statistical machine translation using word sense disambiguation. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Prague, Czech Republic.