Chinese Shallow Semantic Parsing Based on Multi-method of Machine Learning

Authors

  • Fucheng Wan Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China
  • Xiangzhen He Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China
  • Dongjiao Zhang Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China
  • Guo Qi Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China
  • Ao Zhu Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China
  • Zhang Lei  Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China
  • Ning Zenan Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China
  • Wang Yicheng Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China

DOI:

https://doi.org/10.13052/jwe1540-9589.19565

Keywords:

Semantic role labelling, multi-method, linear sequence, hierarchical tree, deep learning, modularization

Abstract

With the rapid development of 5G+ information intelligence, higher requirements are put forward for accurate and efficient semantic annotation methods. Semantic role annotation for any single method at present has its obvious and complementary advantages and disadvantages. Therefore, this paper attempts to introduce the above three mainstream and stable annotation methods into each task of semantic role annotation, and designs a Chinese semantic role annotation that integrates multi-method. This method integrates the statistical-based linear sequence method, the rule-based hierarchical tree method and the most advanced deep learning in the four processing modules of semantic role annotation. Multi-level linguistic features are introduced into the feature arrangement of the model to realize the mutual combination of multiple modules. Experiments show that the modular fusion of steps and methods effectively improves the annotation performance of each step of annotation.

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Author Biographies

Fucheng Wan, Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China

Fucheng Wan, (1985-), male, China, Liaoning Province, Northwest Minzu University, associate professor, master’s tutor, research direction contain natural language processing, Tibetan-Chinese machine translation, information extraction, automatic question and answer research. Published more than 20 core papers, writing 4 books, access to patents and software copyright more than 10 items.

Xiangzhen He, Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China

Xiangzhen He, (1977-) China, Ningxia Province, Northwest Minzu University, associate professor, master’s tutor, research direction contain natural language processing and motion capture. Published more than 40 core papers.

Dongjiao Zhang, Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China

Dongjiao Zhang, born in 1996 in Qitaihe, Heilongjiang Province, is now a graduate student in Northwest University for nationalities. Her research direction is data visualization and has published a paper.

Guo Qi, Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China

Guo Qi, graduate student of China National Information Technology Research Institute, Northwest University for Nationalities, whose main research interests are natural language processing and information extraction.

Ao Zhu, Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China

Ao Zhu, graduate student of China National Institute of Information Technology, Northwest University for Nationalities. He is from shanxi Province. His research direction is shallow semantic analysis, and have published a paper and applied for a soft copy.

Zhang Lei , Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China

Zhang Lei is a graduate student at Northwest University for Nationalities since 2019. He researches automatic question answering technology. He has published a paper and a software book.

Ning Zenan, Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China

Ning Zenan, born in 1996 in Yuncheng City, Shanxi Province. I was a graduate student in Northwest University, and published a research paper in the direction of national science and technology.

Wang Yicheng, Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University, Lanzhou, China

Wang Yicheng, from Luliang, Shanxi Province. He obtained a master’s degree from Northwest University for nationalities. His research direction is semantic role analysis. He has published two CSCD papers.

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Published

2020-10-28

How to Cite

Wan, F. ., He, X., Zhang, D. ., Qi, G. ., Zhu, A. ., Lei , Z., Zenan, N. ., & Yicheng, W. . (2020). Chinese Shallow Semantic Parsing Based on Multi-method of Machine Learning. Journal of Web Engineering, 19(5-6), 685–706. https://doi.org/10.13052/jwe1540-9589.19565

Issue

2020: Vol 19 Iss 5-6

Section

Articles