Ambiguity Resolution : An Analytical Study

Authors

  • Prashant Y. Itankar  PhD Scholar, Department of Computer Science and Engineering, MPU Bhopal, Madhya Pradesh, India
  • Dr. Nikhat Raza  Associate Professor Department of Computer Science and Engineering, MPU Bhopal, Madhya Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT2062135

Keywords:

Natural Language, Ambiguity, Word Sense Disambiguation.

Abstract

Natural language processing (NLP) is very much needed in today’s world to enhance human-machine interaction. It is an important concern to process textual data and obtain useful and meaningful information from these texts. NLP parses the texts and provides information to machine for further processing. The present status of NLP’s computational process of identifying the meaning (sense) of a word in a particular context is ambiguous, where the meaning of word in the context is not clear and may point to multiple senses. Ambiguity in understanding correct meaning of texts is hampering the growth and development in various fields of Natural language processing applications like Machine translation, Human Machine interface etc. The process of finding the correct meaning of the ambiguous texts in the given context is called as word sense disambiguation (WSD). WSD is perceived as one of the most challenging problem in the Natural language processing community and is still unsolved. It is evident that different ambiguities exist in natural languages and researchers are contributing to resolve the problem in different languages for successful disambiguation. These ambiguities must be resolved in order to understand the meaning of the text and help to boost NLP processing and applications. Objective is to investigate how WSD can be used to alleviate ambiguities, automatically determine the correct meaning of the ambiguous text and help to boost NLP processing and applications. Resolving ambiguity for translation involves working with various natural language processing techniques to investigate the structure of the languages, availability of lexical resources etc. Word Sense Disambiguation (WSD) in the field of computing linguistics is an area which is still unsolved. This paper focus on the in-depth analysis of such ambiguity, issues in Language Translation, how WSD resolves the ambiguity and contribute towards building a framework.

References

  1. Sharma P., Joshi N.; “ Knowledge based Method for word sense disambiguation by using Hindi wordnet”, Engineering, Technology and Applied science research, Volume 9, No. 02,2019.
  2. Salodkar A., Nagwanshi M., Gopchandani B.; “Supervised Approach to word sense disambiguation”, published semantic scholar, 2019.
  3. Saif A., Omar N., Zainodin U., Aziz J.; “Building sense tagged corpususingwikipedia for supervised word sense disambiguation”, Elsevier, 2018
  4. Marvin R., Koehn P.; “Exploring word sense disambiguation abilities of Neural Machine translation systems”, Proceedings of AMTA 2018, Vol 1, pp125-131.
  5. Chaplot Devendra Singh , Ruslan Salakhutdinov, “Knowledge- based Word Sense Disambiguation using Topic Models”, Association for the Advancement of Artificial Intelligence (www.aaai.org), 2018,
  6. Garje G., Kharate G., “ Survey of machine translation systems in India. “International Journal on Natural Language computing. Vol. 02, No.4, October 2013.
  7. Firal O, Cho K., Bengio Y; “ Multiway multilingual neural machine translation with a shared attention mechanism”,Proceedings of the NAACL-HLT 2016, pp. 866-875.
  8. Navigli Roberto; “Word sense disambigunation: A survey”, Computing surveys, Vol. 34, No. 2, Article 10, 2009.
  9. Pal A., Saha D.; " Word sense disambiguation: A survey", International Journal of Control theory and computer modelling, Vol. 5, No. 3,
  10. Lesk Michael; "Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an icecream cone”, Procesdings of the 5th Annual International conference on Systems Documentation, NewYork, pp. 24-26, 1986.
  11. Banerjee Satanjeev and Ted Pedersen; "An adapted Lesk algorithm for word sense disambiguation using WordNet." Computational Linguistics and intelligent text processing. Springer Berlin Heidelberg, 2002. 136-145.
  12. Resnik Philip Stuart; "Selection and information: a class-based approach to lexical relationships." IRCS Technical Reports Series, 1993.
  13. Agirre Eneko and German Rigau; “ A proposal for word sense disambiguation using conceptual distance” arXiv cmplg/9510003.1995.
  14. Fu Ruiji, "Learning semantic hierarchies: A continuous vector space approach." IEEE Transactions on Audio, Speech, and Language Processing 23.3, 2015 pp. 461-471.
  15. Resnik Philip; "Using information content to evaluate semantic similarity in a taxonomy." arXiv preprint cmp-lg/9511007, 1995.
  16. Al-Bayaty, Boshra F. Zopon, and Shashank Joshi; "Comparative Analysis between Naïve Bayes Algorithm and Decision Tree to Solve WSD Using Empirical Approach." Lecture Notes on Software Engineering 4.1, 2016.
  17. Agirre Eneko, and David Martinez; "Exploring automatic word sense disambiguation with decision lists and the Web." Proceedings of the COLING-2000 Workshop on Semantic Annotation and Intelligent Content. Association for Computational Linguistics, 2000.
  18. Escudero, Gerard, Lluís Màrquez and German Rigau; "Naive Bayes and exemplar-based approaches to word sense disambiguation revisited." arXiv preprint cs/0007011, 2000.
  19. Chaplot Devendra Singh, Pushpak Bhattacharyya and Ashwin Paranjape; "Unsupervised Word Sense Disambiguation Using Markov Random Field and Dependency Parser." In AAI, pp. 2217-2223, 2015.
  20. Navigli Roberto and Mirella Lapata; "Graph Connectivity Measures for Unsupervised Word Sense Disambiguation." IJCAI, 2007.
  21. Yarowsky David; "Unsupervised WSD rivaling supervised methods."Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, Massachusetts Institute of Technology, Cambridge, MA., 1995.
  22. Lin Dekang; "Automatic retrieval and clustering of similar words."Proceedings of the 17th international conference on Computational linguistics-Volume 2. Association for Computational Linguistics, 1998.
  23. Jain Amita and D. K. Lobiyal; "Unsupervised Hindi word sense disambiguation based on network agglomeration." Computing for Sustainable Global Development (INDIACom), 2nd International Conference on. IEEE, 2015.
  24. RudraMurthy V, Sudha Bhingardive Dhirendra Singh, Hanumant Redkar, an Pushpak Bhattacharyya;"Unsupervised Most Frequent Sense Detection using Word Embeddings.", ACL, 2015.
  25. Bhingardive Sudha, Samiulla Shaikh and Pushpak Bhattacharyya; "Neighbors Help: Bilingual Unsupervised WSD Using Context." ACL,2013.
  26. Singh Satyendr and Tanveer J. Siddiqui; "Role of Semantic Relations in Hindi Word Sense Disambiguation." Procedia Computer Science 46 , 2015 pp. 240-248.
  27. Agarwal Mohini and Jyoti Bajpai; “Correction based Word Sense Disambiguation” Contemporary Computing (IC3), 2014 7th International Conference on IEEE,2014.
  28. Singh S., Singh V, Siddhiqui T., “Hindi WSD using semantic related measures”, Springer, 2013, pp. 247-256.
  29. Jain Abhishek , Suneel Yadav and Devendra Tayak; “ Measuring context-meaning for open class words in Hindi language"Contemporary Computing (IC3), 2013 6th International Conference on IEEE, 2013.
  30. Singh Satyendr and Tanveer J. Siddiqui; "Evaluating effect of context window size, stemming and stop word removal on Hindi word sense disambiguation. “ Information Retrieval and Knowledge Mangement (CAMP), 2012 International Conference on. IEEE, 2012.
  31. Sinha M., Kumar M. Pande P., Kashyap L., Bhattacharya P., “ Hindi Word Sense disambiguation. “ International Symposium on Machine Translation, Natural Language Processing and Translation Support Systems, Delhi, India 2014.
  32. Singh Satyendr and Tanveer J. Siddiqui; "Role of Karaka Relations in Hindi Word Sense Disambiguation." Journal of Information Technology Research (JTTR) 8.3(20150, 21-42.
  33. Umrinder Pal, Vishal Goyal and Anisha Rani; "Disambiguating Hindi Words Using N-Gram Smoothing Models." International journal of Engineering Science, Issue, 2014,pp. 26-29.
  34. Singh Satyendr and Tanveer J. Siddiqui; "A Supervised Algorithm for Hindi word Sense Disambiguation." International Journal of Systems, Algorithm Applications 3, 2013, pp. 29-32.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2020-04-30

Issue

Volume 6, Issue 2, March-April-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prashant Y. Itankar, Dr. Nikhat Raza, " Ambiguity Resolution : An Analytical Study, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 2, pp.471-479, March-April-2020. Available at doi : https://doi.org/10.32628/CSEIT2062135