Skip to main content

A Crowdsource-Based Approach for Preparing Bangla POS Tagged Corpus

  • Conference paper
  • First Online:
  • 1146 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 814))

Abstract

Automated Parts of Speech Tagging plays a vital role in the natural language processing. For computational Bangla Language Processing, we do not have large-scale Parts of Speech tagged corpus. There are two basic approaches to implement a corpus, by written rules or automated. To implement a rule-based corpus, we need experts in Bangla linguistics and it is also time-consuming. And for the automated corpus, we need a trained corpus, which is currently not available. Crowdsourcing can be served a vital role to fulfill these two requirements. So, in this paper, we proposed a crowd source-based approach to building Bangla Parts of Speech tagged corpus. We have used a standard tag set for Bangla. Raw documents are collected from various newspapers, books, and online site. We first give some example of Parts of Speech and then provide data to people for crowdsourcing. Finally, we analyze the result of the data, and its accuracy is 95%.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
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

Learn about institutional subscriptions

Notes

  1. 1.

    https://translate.google.com/.

  2. 2.

    Pipilika is the first Bangla search engine developed by the students of Shahjalal University of Science and Technology.

References

  1. Quinn, A.J., Bederson, B.B.: Human computation: a survey and taxonomy of a growing field. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM (2011)

    Google Scholar 

  2. Gordon, J., Van Durme, B., Schubert, L.K.: Evaluation of commonsense knowledge with Mechanical Turk. In: Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk. Association for Computational Linguistics (2010)

    Google Scholar 

  3. Gao, Q., Vogel, S.: Consensus versus expertise: a case study of word alignment with mechanical turk. In: Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk. Association for Computational Linguistics (2010)

    Google Scholar 

  4. Jha, M., et al.: Corpus creation for new genres: a crowdsourced approach to PP attachment. In: Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk. Association for Computational Linguistics (2010)

    Google Scholar 

  5. Parent, G., Eskenazi, M.: Clustering dictionary definitions using amazon mechanical turk.. In: Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk. Association for Computational Linguistics (2010)

    Google Scholar 

  6. Skory, A., Eskenazi, M.: Predicting cloze task quality for vocabulary training. In: Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics (2010)

    Google Scholar 

  7. Akkaya, C., et al.: Amazon mechanical turk for subjectivity word sense disambiguation. In: Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk. Association for Computational Linguistics (2010)

    Google Scholar 

  8. Callison-Burch, C., Dredze, M.: Creating speech and language data with Amazon’s Mechanical Turk. In: Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk. Association for Computational Linguistics (2010)

    Google Scholar 

  9. Dr. Muhammad Zafar Iqbal, Rashed: My Friend, ISBN-984-437046-9

    Google Scholar 

  10. Niladri Sekhar Dash, POS tagset for Bangla Document, Microsoft Research India, Aug 2010

    Google Scholar 

  11. Categorizing and Tagging Words. http://www.nltk.org/book/ch05.html Cited 30 Aug 2017

  12. Quick, Draw! https://quickdraw.withgoogle.com/, cited 30 Aug 2017

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Shamim Ehsan or Sadia Tasnim Swarna .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ehsan, S., Swarna, S.T., Ismail, S. (2019). A Crowdsource-Based Approach for Preparing Bangla POS Tagged Corpus. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 814. Springer, Singapore. https://doi.org/10.1007/978-981-13-1501-5_40

Download citation

Publish with us

Policies and ethics