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《山东大学学报(理学版)》 ›› 2021, Vol. 56 ›› Issue (1): 83-90.doi: 10.6040/j.issn.1671-9352.4.2020.131

• • 上一篇    

基于Arc-LSTM的人职匹配研究

徐菲菲1,许赟杰2   

  1. 1. 上海电力学院计算机科学与技术学院, 上海 200090;2. 上海航空工业(集团)有限公司流程与IT平台软件开发BU, 上海 200232
  • 发布日期:2021-01-05
  • 作者简介:徐菲菲(1983— ),女,博士,副教授,研究方向为粗糙集、三支决策.E-mail:xufeifei1983@hotmail.com
  • 基金资助:
    上海市自然科学基金资助项目(19ZR1420800)

Research on matching resumes and positions based on Arc-LSTM

XU Fei-fei1, XU Yun-jie2   

  1. 1. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, China;
    2. Process and IT Platform Software Development BU, COMAC Shanghai Aviation Industrial(Group), Shanghai 200232, China
  • Published:2021-01-05

摘要: 提出了一种基于改进的长短时记忆神经网络(Arc-LSTM)和词嵌入(Word2Vec)模型相结合的自动匹配方法。首先采用连续词袋(continuous bag of words,CBOW)模型提取中文简历文本特征,从而构建词向量,提出一种基于ArcReLU激活函数和LSTM深度神经网络优化的Arc-LSTM网络,利用该网络构建分类模型,实现文本分类。实验证明,提出的模型能有效地提高分类精度和收敛速度,实现中文简历和职位的精准匹配。

关键词: 深度学习, 激活函数, 人职匹配, ArcReLU, Arc-LSTM

Abstract: An automatic scheme is proposed, which combines the deep neural network Arc-LSTM with Word2Vec model. In this paper, the CBOW model is used to extract resume text features. A new deep neural network, Arc-LSTM is put forward, which is optimized by ArcReLU. The experimental results show that Arc-LSTM can effectively improve the classification accuracy and convergence speed, and achieve the exact match between Chinese resumes and positions.

Key words: deep learning, activation function, job matching, ArcReLU, Arc-LSTM

中图分类号: 

  • TP18
[1] KATZ B, LIN J. Selectively using relations to improve precision in question answering[C] //Proceedings of the EACL-2003 Workshop on Natural Language Processing for Question Answering. Budapest: EACL, 2003: 43-50.
[2] 朱倩, 程显毅, 韩飞. 汉语句子语义三维表示模型[J]. 智能系统学报, 2009, 4(2):122-130. ZHU Qian, CHENG Xianyi, HAN Fei. A three-dimensional representative model of Chinese sentence semantics[J]. CAAI Transactions on Intelligent Systems, 2009, 4(2):122-130.
[3] 张宜浩, 朱小飞, 徐传运, 等. 基于用户评论的深度情感分析和多视图协同融合的混合推荐方法[J]. 计算机学报, 2019, 42(6):1316-1333. ZHANG Yihao, ZHU Xiaofei, XU Chuanyun, et al. Hybrid recommendation approach based on deep sentiment analysis of user reviews and multi-view collaborative fusion[J]. Chinese Journal of Computers, 2019, 42(6):1316-1333.
[4] GULCEHRE C, MOCZULSKI M, DENIL M, et al. Noisy activation functions[C] //Proceedings of the 33rd International Conference on Machine Learning. New York: ICML, 2016.
[5] GOMAR S, MIRHASSANI M, AHMADI M. Precise digital implementations of hyperbolic tanh and sigmoid function[C] //Conference on Signals, Systems & Computers. Alberta: IEEE, 2016: 1586-1589.
[6] 廖祥文, 陈泽泽, 桂林, 等. 基于多任务迭代学习的论辩挖掘方法[J].计算机学报, 2019, 42(7):1524-1538. LIAO Xiangwen, CHEN Zeze, GUI Lin, et al. An argumentation mining method based on multi-task iterative learning[J]. Chinese Journal of Computers, 2019, 42(7):1524-1538.
[7] 许赟杰, 徐菲菲. 基于ArcReLU函数的神经网络激活函数优化研究[J]. 数据采集与处理, 2019, 34(3):517-529. XU Yunjie, XU Feifei. Optimization of activation function in neural network based on ArcReLU function[J]. Journal of Data Acquisition & Processing, 2019, 34(3):517-529.
[8] WU H. Global stability analysis of a general class of discontinuous neural networks with linear growth activation functions[J]. Information Sciences, 2009, 179(19):3432-3441.
[9] 李亚超, 熊德意, 张民. 神经机器翻译综述[J]. 计算机学报, 2018, 41(12):2734-2755. LI Yachao, XIONG Deyi, ZHANG Min. A survey of neural machine translation[J]. Chinese Journal of Computers, 2018, 41(12):2734-2755.
[10] 陈建廷, 向阳. 深度神经网络训练中梯度不稳定现象研究综述[J]. 软件学报, 2018, 29(7):2071-2091. CHEN Jianting, XIANG Yang. Survey of unstable gradients in deep neural network training[J]. Journal of Software, 2018, 29(7):2071-2091.
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