Skip to main content
SpringerLink
Log in

A Detailed Analysis of Word Sense Disambiguation Algorithms and Approaches for Indian Languages

  • Conference paper
  • First Online:
Proceedings of Second Doctoral Symposium on Computational Intelligence

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1374))

Abstract

Word sense disambiguation (WSD) could be a difficult exploration-research issue in computational etymology that was perceived toward the commencement of the exact interest in machine translation (MT) and artificial intelligence (AI). WSD is the process of detecting the right meaning of a word with various senses and requires depth knowledge of various sources. Phrases that contain multifunctional words, easily present a different parsing structure and provoke different understanding, and are always referred to as ambiguous. Much effort is required to resolve this problem using machine translation, but the hard work is still continuing. Numerous techniques have been used in disambiguation process and executed on various frames for approximately all dialects. This article presents a detailed analysis of WSD algorithms and their different approaches adopted by researchers in their researches for many Indian languages. In this paper, we present forward an examination of directed, undirected and information-based methodology and calculations accessible in word sense disambiguation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Bahadur, P., & Chauhan, D. S. (2014). Machine translation—A journey. In 2014 Science and information conference (pp. 187–195). IEEE.

    Google Scholar 

  2. Dayal, V. (2004). The universal force of free choice any linguistic variation yearbook 4, 15–40. Retrieved from http//www.ingentaconnect.com; Cruse, D. (1986). Lexical semantics. Introducing lexical relations. Cambridge: Cambridge University Press.

  3. Cruse, D. (1986). Lexical semantics. Introducing lexical relations. Cambridge: Cambridge University Press.

    Google Scholar 

  4. Zelta, E. N. (2014). Ambiguity. In Stanford encyclopedia of philosophy. Retrieved from http://plato.stanford.edu; Navigli, R. (2009). Word sense disambiguation: A survey. ACM Computing Surveys (CSUR), 10.

  5. Navigli, R. (2009). Word sense disambiguation: A survey. ACM Computing Surveys (CSUR), 41(2), 1–69.

    Article  Google Scholar 

  6. Lin, D., and Pantel, P. (2002). Discovering word senses from text. In ACM

    Google Scholar 

  7. Ranjan Pal, A., & Saha, D. (2015). Word sense disambiguation: A survey. International Journal of Control Theory and Computer Modeling, 5(3), 1–16. https://doi.org/10.5121/ijctcm.2015.5301

    Article  Google Scholar 

  8. Haroon, R. P. (2011). Word sense disambiguation-A survey. In Proceedings of the international colloquiums on computer electronics Electrical Mechanical and Civil, (EMC’ 11), ACEEE (pp 58–60). DOI: 02.CEMC.2011.01.582

    Google Scholar 

  9. Niu, C., Li, W., Srihari, R. K., Li, H., & Crist, L. (2004). Context clustering for word sense disambiguation based on modeling pairwise context similarities. In Proceedings of SENSEVAL-3, the third international workshop on the evaluation of systems for the semantic analysis of text (pp. 187–190).

    Google Scholar 

  10. Le, C. A., & Shimazu, A. (2004). High WSD accuracy using Naive Bayesian classifier with rich features. In Proceedings of the 18th Pacific Asia conference on language, information and computation (pp. 105–114).

    Google Scholar 

  11. Saktel, P., & Shrawankar, U. (2013). An improved approach for word ambiguity removal. arXiv preprint arXiv:1304.7282.

  12. Sheth, M., Popat, S., & Vyas, T. (2016). Word sense disambiguation for Indian languages. In International conference on emerging research in computing, information, communication and applications (pp. 583–593). Springer, Singapore.

    Google Scholar 

  13. Chaplot, D. S., & Salakhutdinov, R. (2018). Knowledge-based word sense disambiguation using topic models. In Proceedings of the AAAI conference on artificial intelligence (Vol. 32, No. 1).

    Google Scholar 

  14. Aliwy, A. H., & Taher, H. A. (2019). Word sense disambiguation: Survey study. Journal of Computer Science. Accepted July 2019, Iraq.

    Google Scholar 

  15. Wang, Y., Wang, M., & Fujita, H. (2020). Word sense disambiguation: A comprehensive knowledge exploitation framework. Knowledge-Based Systems, 190, 105030.

    Google Scholar 

  16. Alzubi, O. A., Alzubi, J. A., Alweshah, M., Qiqieh, I., Al-Shami, S., & Ramachandran, M. (2020). An optimal pruning algorithm of classifier ensembles: Dynamic programming approach. Neural Computing and Applications, 32(20), 16091–16107.

    Article  Google Scholar 

  17. Gaudard, L., & Romerio, F. (2020). A conceptual framework to classify and manage risk, uncertainty and ambiguity: An application to energy policy. Energies, 13(6), 1422.

    Article  Google Scholar 

  18. Scarlini, B., Pasini, T., & Navigli, R. (2020). SensEmBERT: Context-enhanced sense embeddings for multilingual word sense disambiguation. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 05, pp. 8758–8765).

    Google Scholar 

  19. Scarlini, B., Pasini, T., & Navigli, R. (2020). With more contexts comes better performance: Contextualized sense embeddings for all-round word sense disambiguation. In Proceedings of the 2020 conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 3528–3539).

    Google Scholar 

  20. Bahadur, P. (2013). English to Sanskrit machine translation-EtranS system. International Journal of Computer Applications & Information Technology, 3(II) (ISSN: 2278–7720).

    Google Scholar 

  21. Banerjee, S., and Pedersen, T. (2002). An adapted Lesk algorithm for word sense disambiguation using WordNet. In Proceedings of the third international conference on intelligent text processing and computational linguistics, Mexico City, February.

    Google Scholar 

  22. Lesk, M. (1986). Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone. In Proceedings of SIGDOC.

    Google Scholar 

  23. Jiang, J. J., & Conrath, D. W. (1997). Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of the 10th research on computational linguistics international conference (pp. 19–33) 5–7 Aug, Taipei, Taiwan.

    Google Scholar 

  24. http://link.springer.com/article/10.1023/A%3A1002674829964#page-1

  25. Parameswarappa, S., & Narayana, V. N. (2013). Kannada word sense disambiguation using decision list. 2(3), 272–278

    Google Scholar 

  26. http://www.academia.edu/5135515/Decision_List_Algorithm_for_WSD_for_Telugu_NLP

  27. Singh, R. L., Ghosh, K., Nongmeikapam, K., & Bandyopadhyay, S. (2014). A decision tree based word sense disambiguation system in Manipuri language. Advanced Computing: An International Journal (ACIJ), 5(4), 17–22.

    Google Scholar 

  28. http://wing.comp.nus.edu.sg/publications/theses/2011/low_wee_urop.pdf

  29. http://www.d.umn.edu/~tpederse/Pubs/naacl01.pdf

  30. Le, C. A., & Shimazu, A. (2004). High WSD accuracy using Naive Bayesian classifier with rich features. In PACLIC 18 (pp. 105–114), 8th–10th Dec 2004, Waseda University, Tokyo.

    Google Scholar 

  31. http://www.cs.upc.edu/~escudero/wsd/00-ecai.pdf

  32. Aung, N. T. T., Soe, K. M., & Thein, N. L. (2011). A word sense disambiguation system using Naïve Bayesian algorithm for Myanmar language. International Journal of Scientific & Engineering Research, 2(9), 1–7.

    Google Scholar 

  33. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.13.9418&rep=rep1&type=pdf

  34. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.154.3476&rep=rep1&type=pdf

  35. http://www.aclweb.org/anthology/W02-1606

  36. http://www.cs.cmu.edu/~maheshj/pubs/joshi+pedersen+maclin.iicai2005.pdf date: 14/05/2015

  37. Erkan, G., & Radev, D. (2004). Lexrank: graph based lexical. Artificial Intelligence Research, 22, 457–479.

    Google Scholar 

  38. Haroon, R. P. (2010). Malayalam word sense disambiguation. In 2010 IEEE international conference on computational intelligence and computing research (pp. 1–4). IEEE.

    Google Scholar 

  39. Kumar, R., & Khanna, R. (2011). Natural language engineering: The study of word sense disambiguation in Punjabi. Research Cell: An International Journal of Engineering Sciences, 1, 230–238. ISSN: 2229–6913.

    Google Scholar 

  40. Das, A., & Sarkar, S. (2013). Word sense disambiguation in Bengali applied to Bengali-Hindi machine translation. In Proceedings of the 10th International Conference on Natural Language Processing (ICON), Noida, India.

    Google Scholar 

  41. Parameswarappa, S., Narayana, V. N., & Yarowsky, D. (2013). Kannada word sense disambiguation using decision list. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 2(3), 272–278.

    Google Scholar 

  42. Kumari, S., & Singh, P. (2013). Optimized word sense disambiguation in Hindi using genetic algorithm. International Journal of Research in Computrer & Communication Technology, 2(7), 445–449.

    Google Scholar 

  43. Anand Kumar, M., Rajendran, S., & Soman, K. P. (2014). Tamil word sense disambiguation using support vector machines with rich features. International Journal of Applied Engineering Research, 9(20), 7609–7620.

    Google Scholar 

  44. Pal, A. R., Saha, D., Naskar, S., & Dash, N. S. (2015). Word sense disambiguation in Bengali: A lemmatized system increases the accuracy of the result. In 2015 IEEE 2nd international conference on recent trends in information systems (ReTIS) (pp. 342–346). IEEE.

    Google Scholar 

  45. Sankar, K. S., Raj, P. R., & Jayan, V. (2016). Unsupervised approach to word sense disambiguation in Malayalam. Procedia Technology, 24, 1507–1513.

    Article  Google Scholar 

  46. Vaishnav, Z. B. (2017). Gujarati word sense disambiguation using genetic algorithm. International Journal on Recent and Innovation Trends in Computing and Communication, 5(6), 635–639.

    Google Scholar 

  47. Shashank, N. S., & Kallimani, J. S. (2017). Word sense disambiguation of polysemy words in kannada language. In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 641–644). IEEE.

    Google Scholar 

  48. pal Singh, V., & Kumar, P. (2018). Naive Bayes classifier for word sense disambiguation of Punjabi language. Malaysian Journal of Computer Science, 31(3).

    Google Scholar 

  49. Borah, P. P., Talukdar, G., Baruah, A. (2019) WSD for assamese language. In J. Kalita, V. Balas, S. Borah, & R. Pradhan (Eds.), Recent developments in machine learning and data analytics. Advances in intelligent systems and computing (Vol. 740). Springer, Singapore. https://doi.org/10.1007/978-981-13-1280-9_11

  50. Vaishnav, Z. B., & Sajja, P. S. (2019). Knowledge-based approach for word sense disambiguation using genetic algorithm for Gujarati. In Information and communication technology for intelligent systems (pp. 485–494). Springer, Singapore.

    Google Scholar 

  51. pal Singh, V., & Kumar, P. (2020). Word sense disambiguation for Punjabi language using deep learning techniques. Neural Computing and Applications, 32(8), 2963–2973.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Technology, SRMU, Lucknow, India

    Archana Sachindeo Maurya & Promila Bahadur

Authors
  1. Archana Sachindeo Maurya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Promila Bahadur
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science Engineering, Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Deepak Gupta

  2. Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Ashish Khanna

  3. Institute of Engineering and Technology, Lucknow, Uttar Pradesh, India

    Vineet Kansal

  4. University of Calabria, Rende, Cosenza, Italy

    Giancarlo Fortino

  5. Department of Information Technology, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Maurya, A.S., Bahadur, P. (2022). A Detailed Analysis of Word Sense Disambiguation Algorithms and Approaches for Indian Languages. In: Gupta, D., Khanna, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Second Doctoral Symposium on Computational Intelligence . Advances in Intelligent Systems and Computing, vol 1374. Springer, Singapore. https://doi.org/10.1007/978-981-16-3346-1_56

Download citation

Publish with us

Policies and ethics

Search

Navigation