Abstract
In this paper, we presented two POS taggers for Mongolian, namely Neural Networks—Multilayer Perceptron and Hidden Markov Model with Viterbi. The accuracy of the former tagger is 95.6%, whereas the latter is 85.6%. Also, we compared the performance of our taggers with the previous works. The Comparison shows that the Neural Network tagger performs better for Mongolian POS tagging than other approaches. Our dataset consists of about 5000 sentences and includes almost 100,000 words for training and testing.
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References
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, pp. 161–163 (2011)
A.K.: Part of Speech Tagging Experiments on Mongolian Language. ICEIC 76 (2013)
Lkhagvasuren, G., Rentsendorj, J. In: Open Information Extraction for Mongolian Language, pp. 299–304 (2020)
Helmut, S.: In: Improvements in Part-of-Speech Tagging with an Application to German, pp. 13–25. Springer, Netherlands, Dordrecht (1999)
Khreich, W., Granger, E., Miri, A., Sabourin, R.: A survey of techniques for incremental learning of hmm parameters. Inf. Sci. 197, 105–130 (2012)
Kupiec, J.: Robust part-of-speech tagging using a hidden markov model. Comput. Speech Lang. 6(3), 225–242 (1992)
Thede, S.M., Harper, M.: A second-order hidden markov model for part-of-speech tagging. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, pp. 175–182 (1999)
Al Shamsi, F., Guessoum, A.: A hidden markov model-based pos tagger for arabic. In: Proceeding of the 8th International Conference on the Statistical Analysis of Textual Data, pp. 31–42. France (2006)
Kumawat, D., Jain, V.: Pos tagging approaches: a comparison. Int. J. Comput. Appl. 118, 32–38 (2015)
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) (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), pp. 1064–1074. Berlin, Germany, Association for Computational Linguistics (2016)
JĂłzefowicz, R., Vinyals, O., Schuster, M., Shazeer, N., Wu, Y.: Exploring the limits of language modeling. CoRR (2016). ArXiv:abs/1602.02410
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)
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Lkhagvasuren, G., Rentsendorj, J., Bukhsuren, E., Namsrai, OE. (2023). Comparison of Different Part-of-Speech Tagging Techniques for Mongolian. In: Weng, S., Shieh, CS., Tsihrintzis, G.A. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIHMSP 2022. Smart Innovation, Systems and Technologies, vol 341. Springer, Singapore. https://doi.org/10.1007/978-981-99-0605-5_9
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