An Efficient Cross-Lingual BERT Model for Text Classification and Named Entity Extraction in Multilingual Dataset

Authors

  • Asoke Nath  Department of Computer Science, St. Xavier's College (Autonomous), Kolkata, West Bengal, India
  • Debapriya Kandar  Department of Computer Science, St. Xavier's College (Autonomous), Kolkata, West Bengal, India
  • Rahul Gupta  Department of Computer Science, St. Xavier's College (Autonomous), Kolkata, West Bengal, India

DOI:

https://doi.org/10.32628/CSEIT217353

Keywords:

Natural Language Processing, BERT, Transformers, Multilingual NER.

Abstract

In recent times, with the rise of the internet, everyone is being bombarded with tons of information and data from various sources like websites, blogs and articles, social media posts and comments, e-news portals etc. Now all these data are mostly unstructured. In this paper, the authors have tried to explore the efficiency of the cross-lingual BERT model i.e. M-BERT for text classification and named entity extraction on multilingual data. The authors have used datasets of three different languages namely: French, German and Portuguese to evaluate the model performance.

References

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

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Published

2021-06-30

Issue

Volume 7, Issue 3, May-June-2021

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]
Asoke Nath, Debapriya Kandar, Rahul Gupta, " An Efficient Cross-Lingual BERT Model for Text Classification and Named Entity Extraction in Multilingual Dataset" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.280-286, May-June-2021. Available at doi : https://doi.org/10.32628/CSEIT217353