Abstract
The purpose of this paper is to present a simple neural networks model—multilayer perceptron for Mongolian part-of speech tagging. We used about 1400 manually tagged sentences for training and testing from Mongolian Penn Treebank. The performance of the model is 80.78% which we consider a promising result. Also, another contribution of this work is that we make our testing data online for the sake of the development of Mongolian part-of-speech tagging research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
One of the autonomous regions of China.
- 2.
References
Narayan, R., Chakraverty, S., Singh, V.: Neural network based parts of speech tagger for hindi. IFAC Proceedings Volumes 47(1) (2014) 519 – 524 3rd International Conference on Advances in Control and Optimization of Dynamical Systems (2014)
Todi, K.K., Mishra, P., Sharma, D.M.: Building a kannada POS tagger using machine learning and neural network models. CoRR abs/1808.03175 (2018)
Józefowicz, R., Vinyals, O., Schuster, M., Shazeer, N., Wu, Y.: Exploring the limits of language modeling. CoRR abs/1602.02410 (2016)
Meftah, S., Semmar, N.: A neural network model for part-of-speech tagging of social media texts. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, European Language Resources Association (ELRA) (May 2018)
Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Trans. Assoc. Comput. Linguist. 4, 357–370 (2016)
Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. 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
Lkhagvasuren, G., Rentsendorj, J. Open Information Extraction for Mongolian Language. (01 2020) 299–304
Bird, Steven, E.L.E.K.: Natural Language Processing with Python. O’Reilly Media Inc (2009)
Jaimai, P., Chimeddorj, O.: Part of speech tagging for Mongolian corpus 09, 103–106 (2009)
Zoljargal Munkhjargal, P.J.: Mongolian Trigram Part-of-Speech Tagger, 161–163 (2011)
Khude,r A.: Part of Speech Tagging Experiments on Mongolian Language. ICEIC 76 (2013)
Helmut, S.: Improvements in Part-of-Speech Tagging with an Application to German, pp. 13–25. Springer, Netherlands, Dordrecht (1999)
Nyamdavaa, O.: Mongolian syntactic annotation for parser development. Master’s thesis, National University of Mongolia, Mongolia (2016)
Marcus, M.P., Marcinkiewicz, M.A., Santorini, B.: Building a large annotated corpus of English: The penn treebank. Comput. Linguist. 19(2), 313–330 (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lkhagvasuren, G., Rentsendorj, J., Namsrai, OE. (2021). Mongolian Part-of-Speech Tagging with Neural Networks. In: Pan, JS., Li, J., Ryu, K.H., Meng, Z., Klasnja-Milicevic, A. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 212. Springer, Singapore. https://doi.org/10.1007/978-981-33-6757-9_15
Download citation
DOI: https://doi.org/10.1007/978-981-33-6757-9_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6756-2
Online ISBN: 978-981-33-6757-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)