基于字符级截断式循环神经网络的人名国籍识别  

Character-Based Disconnected Recurrent Neural Network for Name Nationality Identification

作　　者：张钰莎 张礼明 蒋盛益[2] ZHANG Yusha;ZHANG Liming;JIANG Shengyi(School of Electronic Information,Hunan Institute of Information Technology,Changsha 410151;Eastern Language Processing Center,Guangdong University of Foreign Studies,Guangzhou 510006)

机构地区：[1]湖南信息学院电子信息学院,长沙410151 [2]广东外语外贸大学广州市非通用语种智能处理重点实验室,广州510006

出　　处：《模式识别与人工智能》2019年第4期369-375,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61572145);湖南省教育科学“十三五”规划课题(No.XJK18CGD044)资助~~

摘　　要：人名是反映用户国籍的关键信息,不同国籍的人名在结构和组成成分方面存在差异性和关联性。目前,基于人名的国籍识别研究工作大部分将人名切分成多个独立的字符单元,忽略字符间微妙的搭配和序列关系。针对上述问题,文中提出基于字符级截断式循环神经网络的人名国籍识别模型,将人名通过滑动窗口的方式截断成多个子序列,利用长短期记忆单元模型学习不同子序列内部的字符组合关系,通过平均池化操作聚合所有子序列信息,获取最终的人名向量表示。最后根据该人名向量实现用户的国籍识别。截断式的子序列有利于模型更关注人名内部的细微差异。在Olympic运动员和Aminer学者数据集上的实验表明,文中模型性能较优。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.

关 键 词：国籍识别 用户画像 字符级表示模型 循环神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]