[1]王嘉文,王传栋,杨雁莹.一种中文人名识别的训练架构[J].计算机技术与发展,2018,28(07):53-57.[doi:10.3969/ j. issn.1673-629X.2018.07.012]
　WANG Jia-wen,WANG Chuan-dong,YANG Yan-ying.A Training Framework for Chinese Name Recognition[J].,2018,28(07):53-57.[doi:10.3969/ j. issn.1673-629X.2018.07.012]
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一种中文人名识别的训练架构()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年07期
页码:
53-57
栏目:
智能、算法、系统工程
出版日期:
2018-07-10

文章信息/Info

Title:
A Training Framework for Chinese Name Recognition
文章编号:
1673-629X(2018)07-0053-05
作者:
王嘉文1 ; 王传栋1 ; 杨雁莹2
1. 南京邮电大学 计算机学院,江苏 南京 210023;
2. 南京森林警察学院,江苏 南京 210023
Author(s):
WANG Jia-wen 1 ; WANG Chuan-dong 1 ; YANG Yan-ying 2
1. School of Computer and Software,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;
2. Nanjing Forest Police College,Nanjing 210023,China
关键词:
自然语言处理; 深度学习; 神经网络; 中文人名识别
Keywords:
natural language processing; deep learning; neural networks; Chinese name recognition
分类号:
TP391
DOI:
10.3969/ j. issn.1673-629X.2018.07.012
文献标志码:
A
摘要:
中文人名识别作为中文语言处理的一项关键技术,广泛应用于文本挖掘、语义分析、机器翻译等领域。 在数据日趋海量化和异构化的当今社会,对于中文人名进行命名实体识别已经成为现阶段中文自然语言处理的研究热点之一。 由于现有方法大多依赖于先验的领域知识和工程化的特征,识别模型常需要研究人员的大量语言学知识。 为了减少甚至忽略对这些工程化的特征的依赖,旨在建立一种较为灵活的深度神经网络架构,通过对大规模未标记语料的内部表示的学习,使得系统减少甚至忽略这些工程化特征的影响,采用无监督的方法进行中文人名识别。 实验结果表明,该模型不但性能良好,而且不需要过多的计算资源,在中文人名识别的应用中具有良好的效果。
Abstract:
Chinese name recognition,as a key technology in Chinese language processing,is widely used in text mining,semantic analysis,machine translation and other fields. The data are becoming massive and heterogeneous in today’s society,so the named entity recognition for Chinese names has become one of the hotspots of Chinese natural language processing at this stage. Identification model often requires a large number of linguistic knowledge of the researchers because most of the existing methods rely on transcendental domain knowledge and engineering characteristics. In order to reduce or even ignore the dependence on these engineering features,we aim to establish a more flexible deep neural network architecture which can be through the large-scale unmarked corpus of the internal representation of learning,making the system reduce or even ignore the impact of these engineering features and using the unsupervised method for Chinese name recognition. Experiment shows that the model not only has excellent performance but also does not need too much computing resources,with good effect in the Chinese name recognition application.

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更新日期/Last Update: 2018-08-27