Details
Title
Deep Learning based Tamil Parts of Speech (POS) TaggerJournal title
Bulletin of the Polish Academy of Sciences Technical SciencesYearbook
2021Volume
69Issue
6Affiliation
Anbukkarasi, S. : Department of Computer Science and Engineering, Kongu Engineering College, India ; Varadhaganapathy, S. : Department of Information Technology, Kongu Engineering College, IndiaAuthors
Keywords
POS tagging ; deep learning model ; natural language processing ; Bi-LSTMDivisions of PAS
Nauki TechniczneCoverage
e138820Bibliography
- R. Rajimol and V.S. Anoop, “A framework for named entity recognition for Malayalam – A Comparison of different deep learning ar- chitectures,” Nat. Lang. Process. Res., vol. 1, pp. 14–22, 2020.
- Y. Liu et al., “Multilingual denoising pre-training for neural machine translation,” Trans. Assoc. Comput. Ling., vol. 8, pp. 726–742, 2020.
- K.S. Kalaivani and S. Kuppuswami, “Exploring the use of syntactic dependency features for document-level sentiment classification,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, pp. 339–347, 2019, doi: 10.24425/bpas.2019.128608.
- S. Anbukkarasi and S. Varadhaganapathy, “Machine Translation (MT) techniques for Indian Languages,” Int. J. Recent Technol. Eng., vol. 8, 86–90, 2019, doi: 10.35940/ijrte.B1015.0782S419.
- E. Brill, “A simple rule-based part of speech tagger,” in Proc. 3rd Conference on Applied Natural Language Processing, Association for Computational Linguistics, 1992, pp. 152–155, doi: 10.3115/974499.974526.
- T. Berg-Kirkpatrick, A. Bouchard-Côté, J. DeNero, and D. Klein, “Painless unsupervised learning with features,” in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2010, pp. 582–590.
- N. Bölücü and B. Can, “Joint PoS tagging and stemming for agglutinative languages,” in Proc. of the International Conference on Com- putational Linguistics and Intelligent Text Processing, 2017, pp. 110–122.
- P. Arulmozhi, T. Pattabhi R.K. Rao and L. Sobha, “A Hybrid POS Tagger for a Relatively Free Word Order Language,” [Online]. Available https://www.academia.edu/23833233/A_Hybrid_POS_Tagger_for_a_Relatively_Free_Word_Order_Language (Accessed: Jan, 10, 2021)
- J. Singh, N. Joshi, and I. Mathur, “Development of Marathi part of speech tagger using statistical approach,” in Proc. of International Conference on Advances in Computing, Communications and Informatics, 2013, pp. 1554–1559.
- M. Ramanathan, V. Chidambaram, and A. Patro, “An Attempt at Multilingual POS Tagging for Tamil,” [Online]. Available http://pages. cs.wisc.edu/~madhurm/CS769_final_report.pdf (Accessed: Jan. 10. 2021).
- N. Bölücü, B. Can, “A Cascaded Unsupervised Model for PoS Tagging,” ACM Trans. Asian Low-Resour. Lang. Inf. Process., vol. 20, pp. 1–23, Mar. 2021, doi: 10.1145/3447759.
- S. Adinarayanan and N.S. Ranjaniee, “Part-of speech tagger for sanskrit. A state of art survey,” Int. J. Appl. Eng. Res., vol. 10, pp. 24173– 24178, 2015. doi: 10.37200/IJPR/V23I1/PR190243.
- H. Ali, Unsupervised Parts-of-Speech Tagger for the Bangla language, Department of Computer Science. University of British Colum- bia, 2010. [Online]. Available: https://www.cs.ubc.ca/~carenini/TEACHING/CPSC503-09/FINAL-REPORTS-08/hammad-report1.1.pdf (Accessed: Jan. 10. 2021).
- K. Stratos, M. Collins, and D. Hsu, “Unsupervised part-of-speech tagging with anchor hidden markov models,” Trans. Assoc. Comput. Ling., vol. 4, pp. 245–257, 2016, doi: 10.1162/tacl_a_00096.
- K. Sarkar and V. Gayen, “A trigram HMM-based POS tagger for Indian languages,” in Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), 2013, pp. 205–212.
- M. Banko and R.C. Moore, “Part of speech tagging in context,” in Proc. 20th International Conference on Computational Linguistics, 2004, 556, doi: 10.3115/1220355.1220435.
- Z. Huang, W. Xu, and K. Yu, “Bidirectional lstm-crf models for sequence tagging,” 2015. [Online]. Available: https://arxiv.org/ abs/1508.01991 (Accessed: Jan. 10. 2021).
- M. Thayaparan, S. Ranathunga, and U. Thayasivam, “Graph Based Semi-Supervised Learning for Tamil POS Tagging.” FIRE 2014, [Online]. Available: https://aclanthology.org/L18-1624.pdf (Accessed: Jan. 10. 2021).
- B. Plank, A. Søgaard, and Y. Goldberg, “Multilingual part-of-speech tagging with bidirectional long short-term memory models and auxiliary loss,” in Proc. 54th Annu. Association for Computational Linguistics, 2016, pp. 412–418.
- M. Rajasekar and A. Udhayakumar, “POS Tagging Using Naive Bayes Algorithm For Tamil,” Int. J. Sci. Eng. Technol. Res., vol. 9, pp. 574–578, Feb. 2020.
- J. Singh, L. Singh Garcha, and S. Singh, “A Survey on Parts of Speech Tagging for Indian Languages,” Int. J. Adv. Res. Comput. Sci. Software Eng., vol. 7, no. 4, Apr. 2017.
- V. Dhanalakshmi, A.M. Kumar, and K.P. Soman, and S. Rajendran, “POS Tagger and Chunker for Tamil Language,” Proceedings of the 8th Tamil Internet Conference, Cologne, Germany, 2009.
- K.K. Akhil, R. Rajimol, and V.S. Anoop, “Parts-of-Speech tagging for Malayalam using deep learning techniques,” Int. J. Inf. Technol., vol. 12, pp. 741–748, 2020, doi: 10.1007/s41870-020-00491-z.
- E. Lukasik et al., “Recognition of handwritten Latin characters with diacritics using CNN,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 1, p. e136210, 2021, doi: 10.24425/bpasts.2020.136210.
- D. Andor et al., “Globally normalized transition-based neural networks,” in Proc. 54th Annu. Association for Computational Linguistics, Berlin, Germany, 2016, pp. 2442–2452.
- M. Yan et al., “A deep cascade model for multi-document reading comprehension,” in Proc. of The Thirty-Third AAAI Conference on Artificial Intelligence, 2018, pp. 7354–7361.
- P. Wang, Y. Qian, F.K. Soong, L. He, and Z. Hai, “Part-of-speech tagging with bidirectional long short-term memory recurrent neural network,” [Online]. Available: https://arxiv.org/abs/1510.06168v1
- Keras, [Online] Available: https://keras.io/ (Accessed: 30.03.21).