基于字符级截断式循环神经网络的人名国籍识别

doi:10.16451/j.cnki.issn1003-6059.201904010

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Abstract Personal name is viewed as a strong indicator of inferring the nationality of the user. Generally, personal names reveal the differentiation and correlation of naming conventions among different nationalities. In the current research, personal name features are extracted by cutting off name strings into a set of independent n-gram units, while subtle relationships between characters are not explored. Therefore, a character-based disconnected recurrent neural network is proposed to capture subtle features among personal names in this paper. Concretely, a set of fragments is derived from name strings by order using a slice window. Then, long short-term memory units are utilized to learn information of each fragment, and they are aggregated via mean-pooling operation to obtain the whole name representation for nationalities prediction of users. Disconnected fragments enable model to focus on subtle features among different personal names. Experiments on Olympic dataset and Aminer dataset show that the proposed model outperforms the existing models and the performance is satisfactory.

Key words： Nationality Identification User Profiling Character Modeling Recurrent Neural Network

Received: 24 January 2019

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61572145), The Thirteenth Five-Year Plan Project of Educational Science in Hunan Province(No.XJK18CGD044)

About author:: ZHANG Yusha, master, associate profe-ssor. Her research interests include data mining and natural language processing.ZHANG Liming(Corresponding author), master student. His research interests include natural language processing.JIANG Shengyi, Ph.D, professor. His research interests include data mining and natural language processing.

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	ZHANG Yusha
	ZHANG Liming
	JIANG Shengyi

Cite this article:

ZHANG Yusha,ZHANG Liming,JIANG Shengyi. Character-Based Disconnected Recurrent Neural Network for Name Nationality Identification[J]. , 2019, 32(4): 369-375.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201904010 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I4/369

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