Uyghur morphological segmentation with bidirectional GRU neural networks

doi:10.16511/j.cnki.qhdxxb.2017.21.001

PDF(1041 KB)

Journal of Tsinghua University(Science and Technology) ›› 2017, Vol. 57 ›› Issue (1) : 1-6. DOI: 10.16511/j.cnki.qhdxxb.2017.21.001

COMPUTER SCIENCE AND TECHNOLOGY

Uyghur morphological segmentation with bidirectional GRU neural networks

{{article.zuoZhe_EN}}

Author information +

History +

Abstract

Information processing of low-resource, morphologically-rich languages such as Uyghur is critical for addressing the language barrier problem faced by the One Belt and One Road (B&R) program in China. In such languages, individual words encode rich grammatical and semantic information by concatenating morphemes to a root form, which leads to severe data sparsity for language processing. This paper introduces an approach for Uyghur morphological segmentation which divides Uyghur words into sequences of morphemes based on bidirectional gated recurrent unit (GRU) neural networks. The bidirectional GRU exploits the bidirectional context to resolve ambiguities and model long-distance dependencies using the gating mechanism. Tests show that this approach significantly outperforms conditional random fields and unidirectional GRUs. This approach is language-independent and can be applied to all morphologically-rich languages.

Key words

bidirectional gated recurrent unit / neural network / Uyghur / morphological segmentation

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

ABUDUKELIMU Halidanmu, CHENG Yong, LIU Yang, SUN Maosong. Uyghur morphological segmentation with bidirectional GRU neural networks[J]. Journal of Tsinghua University(Science and Technology). 2017, 57(1): 1-6 https://doi.org/10.16511/j.cnki.qhdxxb.2017.21.001

References

"[1] Orhun M, Tanguǎ C, Adal? E. Rule based analysis of the Uyghur nouns[J]. International Journal on Asian Language Processing, 2009, 19(1):33-43. [2] Sami V, Peter S, Arne G et al. Morfessor 2.0:Python Implementation and Extensions for Morfessor Baseline, ISBN 978-952-60-5501-5[R]. Helsinki:Aalto University, 2013. [3] Lafferty J, McCallum A, Pereira F. Conditional random fields:probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the 18th International Conference on Machine Learning. Williamstown, MA, USA:Morgan Kaufmann, 2001:282-289. [4] Ruokolainen T, Kohonen O, Virpioja S et al. Supervised morphological segmentation in a low-resource learning setting using conditional random fields[C]//Proceeding of the Seventeenth Conference on Computational National Language Learning. Sofia, Bulgaria:Association for Computational Linguistics, 2013:8-9. [5] Aisha B, SUN Maosong. A statistical method for Uyghur tokenization[C]//International Conference on Natural Language Processing and Knowledge Engineering. Dalian:IEEE, 2009:24-27. [6] 买热哈巴·艾力, 姜文斌, 王志洋, 等. 维吾尔语词法分析的有向图模型[J]. 软件学报, 2012, 23(12):3115-3129. Aili M, JIANG Wenbin, WANG Zhiyang, et al. Directed graph model of Uyghur morphological analysis[J]. Journal of Software, 2012, 23(12):3115-3129. (in Chinese) [7] Wumaier A, Tian S. Conditional random fields combined FSM stemming method for Uyghur[C]//International Conference on Computer Science and Information Technology. Beijing:IEEE, 2009:8-11. [8] Ablimit M, Kawahara T, Pattar A, et al. Stem-affix based Uyghur morphological analyzer[J]. International Journal of Future Generation Communication and Networking, 2016, 9(2):59-72. [9] Chung J, Gulcehre C, Cho K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[Z/OL]. (2014-12-11). https://arxiv.org/abs/1412.3555. [10] Chen X, Qiu X, Zhu C et al. Long short-term memory neural networks for Chinese word segmentation[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon, Portugal:Association for Computational Linguistics, 2015:17-21. [11] Yao Y, Huang Z. Bi-directional LSTM recurrent neural network for Chinese word segmentation[Z/OL]. (2016-02-16). http://arxiv.org/abs/1602.04874. [12] Morita H, Kawahara D, Kurohashi S. Morphological analysis for unsegmented languages using recurrent neural network language model[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon, Portugal:Association for Computational Linguistics, 2015:17-21. [13] Wang L, Cao Z, Xia Y, et al. Morphological segmentation with window ISTM neural networks[C]//Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. Phoenix, AZ, USA:Association for the Advancement of Artificial Intelligence, 2016:2842-2848. [14] Wang P, Qian Y, Soong F, et al. Part-of-speech tagging with bidirectional long short-term memory recurrent neural network[Z/OL]. (2015-10-21). http://arxiv.org/abs/1510.06168. [15] Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult[J]. IEEE Transactions on neural networks, 1994, 5(2):157-166. [16] Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780. [17] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[Z/OL]. (2014-09-01). https://arxiv.org/abs/1409.0473 [18] Schuster M, Paliwal K. Bidirectional recurrent neural networks[J]. IEEE Transactions on signal processing, 1997, 45(11):2673-2681. [19] Graves A, Jaitly N, Mohamed A. Hybrid speech recognition with deep bidirectional ISTM[C]//2013 IEEE Workshop on Automatic Speech Recognition and Understanding. Olomouc, Czech:IEEE, 2014:8-12."