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Empirical study on character level neural network classifier for Chinese text

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成果类型:
期刊论文
作者:
Chung, Tonglee;Xu, Bin;Liu, Yongbin*;Ouyang, Chunping;Li, Siliang;...
通讯作者:
Liu, Yongbin
作者机构:
[Xu, Bin; Li, Siliang; Chung, Tonglee] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China.
[Ouyang, Chunping; Liu, Yongbin; Luo, Lingyun] Univ South China, Coll Comp Sci & Technol, Hengyang 421001, Peoples R China.
通讯机构:
[Liu, Yongbin] U
Univ South China, Coll Comp Sci & Technol, Hengyang 421001, Peoples R China.
语种:
英文
关键词:
Character level classifier;CNN;Neural network;RNN
期刊:
Engineering Applications of Artificial Intelligence
ISSN:
0952-1976
年:
2019
卷:
80
页码:
1-7
基金类别:
China National High-Tech Project (863) [2015AA015401]; Beijing Key Lab of Networked Multimedia; 973 Program, ChinaNational Basic Research Program of China [2014CB340504]; State Key Program of National Natural Science of ChinaNational Natural Science Foundation of China (NSFC) [61533018]; NSFC-ANR, ChinaFrench National Research Agency (ANR) [61261130588]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61402220, 61502221, 1309007]; State Scholarship Fund of CSC, China [201608430240]; Philosophy and Social Science Foundation of Hunan Province, China [16YBA323]; Tsinghua University Initiative Scientific Research Program, China [20131089256]; Science and Technology Support Program, China [2014BAK04B00]; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2014M550733]; THU-NUS NExT Co-Lab, China; Scientific Research Fund of Hunan Provincial Education DepartmentHunan Provincial Education Department [16C1378]
机构署名:
本校为通讯机构
院系归属:
计算机科学与技术学院
摘要:
Character level models are drawing attention recently. A number of these models have been proposed and shown successful in Natural Language Processing tasks. While most of the models are experimented mainly on English, or other alphabetic languages, a number of problems arise when they applied these models to non-alphabetic language such as Chinese. In this study, we investigated the problems encountered when transferring these models to the Chinese and put forward some solutions. We propose a double embedding neural network model that is also ...

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