Character-Based Disconnected Recurrent Neural Network for Name Nationality Identification
ZHANG Yusha1, ZHANG Liming2, JIANG Shengyi2
1.School of Electronic Information, Hunan Institute of Information Technology, Changsha 410151 2.Eastern Language Processing Center, Guangdong University of Foreign Studies, Guangzhou 510006
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.
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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2019, Vol. 32 
