Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio.International Journal of Applied Engineering Research 10, 9 (2015), 24173-24178. Part-of Speech Tagger For Sanskrit: A State of Art Survey. Sharadha Adinarayanan and N Sri Ranjaniee.The character level implementations of both uni and bidirectional forms of RNN, LSTM and GRU outperformed all world level implementations in terms of accuracy, number of trainable parameters and the storage requirement. We obtained the accuracy score of 97.86% with Bidirectional GRU. The performance of the labeling task was analyzed with the different combinations of hyper-parameters. The character level formulation of the problem reduces the memory requirement compared to the word level implementations and also increases the accuracy of labeling. Therefore, a word to be POS tagged is considered as a sequence of characters and the sequential relationship among the characters in a word is captured with the deep learning algorithms such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) networks, Gate Recurrent Unit (GRU) and their bidirectional versions. This problem is framed as a sequence labeling problem at the character level. In this paper, various deep learning algorithms are used for implementing a POS tagger for Sanskrit. However, less attention was given to the machine learning based POS tagging. In Sanskrit also, one of the oldest languages in the world, many POS taggers were developed. Linguist.Part-of-Speech (POS) tagging is an important task in Natural Language Processing and numerous taggers have been developed for POS tagging in several languages. Marcus, M.P., Marcinkiewicz, M.A., Santorini, B.: Building a large annotated corpus of English: The penn treebank. Master’s thesis, National University of Mongolia, Mongolia (2016) Nyamdavaa, O.: Mongolian syntactic annotation for parser development. Helmut, S.: Improvements in Part-of-Speech Tagging with an Application to German, pp.
Khude,r A.: Part of Speech Tagging Experiments on Mongolian Language. Zoljargal Munkhjargal, P.J.: Mongolian Trigram Part-of-Speech Tagger, 161–163 (2011) Jaimai, P., Chimeddorj, O.: Part of speech tagging for Mongolian corpus 09, 103–106 (2009) (01 2020) 299–304īird, Steven, E.L.E.K.: Natural Language Processing with Python. Our best-performing part of speech tagger for English uses both preceding and following tag context, and many lexical features. We have worked on building probabilistic conditional log-linear models for tagging.
Open Information Extraction for Mongolian Language. Part-of-speech tagging is assigning the correct part of speech (noun, verb, etc.) to words. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, Germany, Association for Computational Linguistics (2016) 1064–1074 Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, European Language Resources Association (ELRA) (May 2018)Ĭhiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Meftah, S., Semmar, N.: A neural network model for part-of-speech tagging of social media texts. Józefowicz, R., Vinyals, O., Schuster, M., Shazeer, N., Wu, Y.: Exploring the limits of language modeling. Todi, K.K., Mishra, P., Sharma, D.M.: Building a kannada POS tagger using machine learning and neural network models. IFAC Proceedings Volumes 47(1) (2014) 519 – 524 3rd International Conference on Advances in Control and Optimization of Dynamical Systems (2014) Narayan, R., Chakraverty, S., Singh, V.: Neural network based parts of speech tagger for hindi.
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