"[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."