Model Construction of Uygur and Korean Morphological Analysis

doi:10.13190/j.jbupt.2017-117

Journal of Beijing University of Posts and Telecommunications ›› 2018, Vol. 41 ›› Issue (1): 88-94.doi: 10.13190/j.jbupt.2017-117

• Papers • Previous Articles Next Articles

Model Construction of Uygur and Korean Morphological Analysis

XU Chun^1,2,3,4, JIANG Tong-hai^1,3, YU Kai⁴, JIANG Wen-bin^2,5

1. Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Xinjiang Key Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China;
4. College of Computer Science and Engineering, Xinjiang University of Finance and Economics, Urumqi 830012, China;
5. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Science, Beijing 100190, China

Published:2018-01-04

Abstract

Abstract: A discriminant model of the graphic structure is established for the morphological analysis of Uighur and Korean language. The model builds the morphological analysis of the sentence into the graphic structure of morphological components, and describes the correlation between the morphological components of the words inside and the morphological components of the adjacent words through flexible and rich feature design. Compared with the traditional linear model, the pattern model is better to consider the linguistic association between the morphological components, and it is expected to achieve higher sentence analysis performance. The experimental results in Uighur and Korean indicate that the graphic model achieves certain performance improvement comparing with the linear model, and the word level accuracy of morphological analysis respectively increases by 4.4 and 2.8 percentage points.

Key words: morphological analysis, agglutinative language, graphic model, linear mode

CLC Number:

TN391.1

XU Chun, JIANG Tong-hai, YU Kai, JIANG Wen-bin. Model Construction of Uygur and Korean Morphological Analysis[J]. Journal of Beijing University of Posts and Telecommunications, 2018, 41(1): 88-94.

References

[1] 赛迪亚古丽·艾尼瓦尔, 向露, 宗成庆, 等. 基于多策略的维吾尔语词干提取方法[J]. 中文信息学报, 2015, 29(5):204-210. Sediyegvl Enwer, Xiang Lu, Zong Chengqing, et al. A multi-strategy approach to uyghur stemming[J]. Journal of Chinese Information Processing, 2015, 29(5):204-210.
[2] 徐春, 杨勇, 蒋同海. 基于机器翻译的维吾尔语形态分析研究[J]. 计算机工程与应用, 2017, 53(14):138-142. Xu Chun, Yang Yong, Jiang Tonghai. Research on machine translation based uyghur morphological analysis[J]. Computer Engineering and Applications, 2017, 53(14):138-142.
[3] 麦热哈巴·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. Mairehaba·AILI. Jiang Wenbin, Wang Zhiyang. Directed graph model of uyghur morphological analysis[J]. Journal of Software, 2012, 23(12):3115-3129.
[4] 张海波, 蔡洽吾, 姜文斌, 等. 基于联合音变还原和形态切分的形态分析方法[J]. 中文信息学报, 2014, 28(6):10-16. Zhang Haibo, Cai Qiawu, Jiang Wenbin. Morphological segmentation model for morphological analysis[J]. Journal of Chinese Information Processing, 2014, 28(6):10-16.
[5] Lee D G, Rim H C. Probabilistic modeling of korean morphology[J]//IEEE Transactions on Audio, Speech, and Language Processing, 2009, 17(5):945-955.
[6] Ablimit M, Kawahara T, Pattar A, et al. Stem-affix based Uyghur morphological analyzer[J]. International Journal of Future Generation Communication and Networking, 2008, 9(2):59-72.
[7] 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:Association for Computational Linguistics, 2013:8-9.
[8] 赵伟, 侯宏旭, 丛伟, 等. 基于条件随机场的蒙古语词切分研究[J]. 中文信息学报, 2010, 24(5):31-35. Zhao Wei, Hou Hongxu, Cong Wei. Research on conditional random fields based mongolian word segmentation[J]. Journal of Chinese Information Processing, 2010, 24(5):31-35.
[9] Jiang W, Mi H, Liu Q. Word lattice reranking for Chinese word segmentation and part-of-speech tagging[C]//Proceedings of the 22^nd International Conference on Computational Linguistics. Manchester:[s.n.], 2008:385-392.
[10] 华却才让. 基于树到串藏语机器翻译若干关键技术研究[D]. 西安:陕西师范大学, 2014.
[11] Michael C. Discriminative training methods for hidden markov models:theory and experiments with perceptron algorithms[C]//Proceedings of EMNLP, 2002, Pennsylvania, USA, 410(21-23):1-8.
[12] Le Zhang. Maximum entropy modeling toolkit for python and C++[EB/OL]. (2011-09-09)[2017-05-06]. http://homepages.inf.ed.ac.uk/lzhang10/maxent_toolkit.html

Model Construction of Uygur and Korean Morphological Analysis

RichHTML

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 3

Recommended Articles

Metrics

Comments

[1]	ZHAO Xia, NI Ying-ting, LI Zhan-ning. Stability Analysis for a Modified Bilinear Model Based on Recursive Least Squares [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(1): 121-124.
[2]	XIAO Ding, JIA Ya-pu, SHI Chuan. Non-liner Energy Consumption Model for Cloud Computing [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2016, 39(1): 107-111.
[3]	. Feedback Control for a Class of Interconnected  Systems Based Fuzzy Bilinear Model [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2011, 34(1): 135-149.