ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2015, Vol. 52 ›› Issue (11): 2451-2459.doi: 10.7544/issn1000-1239.2015.20140808

• 人工智能 • 上一篇    下一篇

基于深度神经网络的有色金属领域实体识别

毛存礼1,余正涛1,沈韬2,高盛祥1,郭剑毅1,线岩团1   

  1. 1(昆明理工大学信息工程与自动化学院 昆明 650500); 2(昆明理工大学材料科学与工程学院 昆明 650093) (maocunli@163.com)
  • 出版日期: 2015-11-01
  • 基金资助: 
    基金项目:国家自然科学基金项目(61262041,61472168,61163004);“科技部创新人才推进计划”中青年科技创新领军人才配套项目(2014HE001);云南省自然科学基金重点项目(2013FA030)

A Kind of Nonferrous Metal Industry Entity Recognition Model Based on Deep Neural Network Architecture

Mao Cunli1, Yu Zhengtao1, Shen Tao2, Gao Shengxiang1, Guo Jianyi1, Xian Yantuan1   

  1. 1(Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500);2(Faculty of Material Science and Engineering, Kunming University of Science and Technology, Kunming 650093)
  • Online: 2015-11-01

摘要: 针对有色金属领域实体识别问题,提出一种基于深度神经网络(deep neural network, DNN)架构的有色金属领域实体识别方法.为能有效获取有色金属领域实体中字符间的紧密结合特征,并回避专业领域中文分词问题,使用神经网络的方法自动学习中文字符embeddings向量化表示作为模型输入.基于降噪自动编码器(denoising autoencoder, DAE)对深度神经网络的每个隐层进行逐层预训练获取用于有色金属领域实体识别的最优特征向量组合,并详细介绍了基于神经语言模型的文本窗口降噪自动编码器预训练及有色金属实体识别的深层网络构建过程.为验证方法的有效性,对有色金属领域产品名、矿产名、地名、组织机构4类实体识别进行实验.实验结果表明,提出的方法对于专业领域的实体识别具有较好的效果.

关键词: 有色金属领域, 深度神经网络, 词汇embeddings, 降噪自动编码器, 实体识别

Abstract: Aimed at entity recognition in the field of nonferrous metal, and oriented the complex and strongly nested structure characteristics of domain entity such as product names, organizations, and placenames, it is proposed a kind of nonferrous metal industry entity recognition model based on deep neural network (DNN) architecture. In order to effectively use the characteristics of tight combination between the characters of domain entity and to bypass the Chinese words segmentation in the professional field, the model uses neural networks to automatically learn the word embeddings vector representation of Chinese characteristics as its inputs. The denoising autoencoder (DAE) of text window makes some pre-training on each DNN hidden layer. The pre-training extracts optimal feature vector combination which will be used in nonferrous metal domain entity recognition. Moreover, we detailedly describe the pre-training process that the denoising autoencoder of text window based on neuron language model makes and the construction process of deep network on nonferrous metal entity recognition. Finally, to validate the method’s effectiveness, we make some experiments of entity recognition on several entity types, such as product names, mineral names and place names in nonferrous metal domain. The experimental results show that the proposed model has good effect on the entity recognition of the professional domain.

Key words: field of nonferrous metal, deep neural network(DNN), word embeddings, denoising autoencoder(DAE), entity recognition

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