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Heuristic Algorithm for Resolving Pronominal Anaphora in Hindi Dialects

  • Seema MahatoEmail author
  • Ani Thomas
  • Neelam Sahu
Conference paper
  • 40 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1082)

Abstract

Artificial intelligence is a necessity for today’s realistic world knowledge. The facts hidden by the anaphoric expressions can be revealed by anaphora resolution only. The relevance of anaphora resolution could not be avoided as its productivity affects the performance of text summarization, automatic question answering system, information extraction, etc. The paper comes up with implementations of algorithms for first, second, and third-person pronouns for Hindi language, with the ability to tackle intersentential anaphora within the scope of 3–5 sentences and subsequently the algorithms can be tailored-up for more sentences. Approximately, 698 sentences were experimented for feature selection of each type of personal pronoun. The proposed algorithms have been tested on the synthetic datasets of 1059 sentences which contain 712 pronominal anaphors out of 781 anaphoric pronouns. The F-measure evaluation for selected corpora gives promising results, indicating that the algorithms are effective in resolving Hindi pronominal anaphora.

Keywords

Pronominal anaphora resolution Machine learning Syntactic rules Morphological knowledge 

Notes

Acknowledgements

The authors gratefully acknowledge the support of Shreyansh Bafna, B.E. (CS), BIT Durg (C.G.) for coding the algorithms in Python and deployment.

References

  1. 1.
    Prasad, R., Strube, M.: Discourse salience and pronoun resolution in Hindi. In: Penn Working Papers in Linguistics, vol. 6(3), 189–208 (2000)Google Scholar
  2. 2.
    Sobha, L., Patnaik, B.N.: Vasisth: An anaphora resolution system for Malayalam and Hindi. In: Symposium on Translation Support Systems (2002)Google Scholar
  3. 3.
    Dutta, K., Prakash, N., Kaushik, S.: Resolving pronominal anaphora in Hindi using Hobbs algorithm. Web J. Form. Comput. Cognit. Linguist. 10 (2008)Google Scholar
  4. 4.
    Lakhmani, P., Singh, S.: Anaphora resolution in Hindi language. Int. J. Inf. Comput. Technol. 3(7), 609–616 (2013)Google Scholar
  5. 5.
    Dakwale, P., Mujadia, V., Sharma, D.M.: A hybrid approach for anaphora resolution in Hindi. In: Proceedings of the 6th International Joint Conference on Natural Language Processing, pp. 977–981. Japan (2013)Google Scholar
  6. 6.
    Mahato, S., Thomas, A.: Exploring semantic information from Hindi dependency treebank for resolving pronominal anaphora. Int. J. Comput. Appl. 0975–8887 (2015)Google Scholar
  7. 7.
    Mahato, S., Thomas, A.: Machine learning approach for resolving pronominal anaphora using Hindi dependency treebank. In: Proceedings of BITCON-2015 Innovations for National Development. IJAERS, vol. IV(II), pp. 155–159 (2015)Google Scholar
  8. 8.
    Mannem, P., Bharati, A.: Introduction to the shallow parsing contest for South Asian languages. In: Proceedings of IJCAI Workshop on Shallow Parsing for South Asian Languages (2009)Google Scholar
  9. 9.
    Bharati, A., Sharma, D.M., Husain, S., Bai, L., Begum, R., Sangal, R.: Anncorra: treebanks for Indian languages, guidelines for annotating Hindi treebank (version 2.0) (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Dr. C.V. Raman UniversityBilaspurIndia
  2. 2.Department of ITBhilai Institute of TechnologyDurgIndia
  3. 3.Department of ITDr. C.V. Raman UniversityBilaspurIndia

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