POS Tagger Model for South Indian Language Using a Deep Learning Approach-Reference-Cited by-同舟云学术

POS Tagger Model for South Indian Language Using a Deep Learning Approach

Published:2022 Issue: Volume: Page:155-167
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
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Author:

Rajani Shree M.,Shambhavi B. R.

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-7985-8_16

Reference22 articles.

1. TDIL. Technology Development for Indian Languages (TDIL), Ministry of Electronics & Information Technology (MeitY), MC & IT, Govt of India. http://tdil-dc.in/

2. Jha GN (2010) The TDIL program and the Indian Language Corpora Initiative (ILCI). In: Proceedings of the seventh conference on international language resources and evaluation (LREC 2010). European Language Resources Association (ELRA)

3. Rajani Shree M, Shambhavi BR (2020) POS tagger model for Kannada text with CRF++ and deep learning approaches. J Discrete Math Sci Cryptogr 23(2):485–493. Taylor & Francis. https://doi.org/10.1080/09720529.2020.1728902

4. Swaroop LR, Rakshit Gowda GS, Hegde S, Sourabh U (2019) Parts of speech tagging for Kannada. In: Proceedings of the student research workshop associated with RANLP-2019, pp 28–31, Varna, Bulgaria, 2–4 Sept 2019

5. Antony PJ, Soman KP (2010) Kernel based part of speech tagger for Kannada. In: 2010 International conference on machine learning and cybernetics, vol 4, pp 2139–2144. IEEE. https://doi.org/10.1109/ICMLC.2010.5580488

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