基于文本挖掘技术的铁路安监人员履职分析

李新琴; 马小宁; 王喆; 邹丹; 杨连报

基于文本挖掘技术的铁路安监人员履职分析

1. 中国铁道科学研究院研究生部, 北京 100081;
2. 中国铁道科学研究院集团有限公司铁路大数据研究与应用创新中心, 北京 100081

基金项目:

中国铁道科学研究院重大课题(2017YJ005)

铁路总公司科技研究开发计划项目(2017X006-B)

详细信息

作者简介:
李新琴,在读博士研究生;马小宁,研究员。

中图分类号: U29;TP39
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- 文章访问数: 72
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出版历程
- 收稿日期: 2018-12-11

Performance analysis of railway safety supervisor based on text mining technology

1. Postgraduate Department, China Academy of Railway Science, Beijing 100081, China;
2. Application Innovation Center for Big Data Technology in Railway, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China

摘要

摘要: 为分析人员工作计划实际落实情况,提供人员考核依据,基于文本挖掘技术进行了铁路安监人员履职分析并设计了文本相似度计算方法。应用双向长短时记忆(BiLSTM)网络与条件随机场(CRF)相结合的BiLSTM-CRF算法实现人员履职计划与写实文本中命名实体的抽取,采用基于知网的概念相似度计算方法计算对应实体间相似度,从而实现人员履职计划内容与实际写实的匹配计算。通过对某铁路局安监人员工作计划与写实文本数据的实验分析,得出BiLSTM-CRF算法针对各命名实体均有90%以上的准确率,人员计划与写实匹配准确度为83%。实验证明,利用BiLSTM-CRF算法与概念相似度结合的文本计算方法进行人员履职分析具有可行性,也可为铁路领域其他短文本相似性计算提供参考。
- 文本相似度 /
- 双向长短时记忆网络 /
- 条件随机场 /
- 命名实体识别 /
- 概念相似度
Abstract: In order to analyze the personnel's work plan and actual implementation, and provide the basis for personnel assessment, based on text mining technology, this paper carried out the performance analysis of railway security supervisors and the text similarity calculation method was designed. BiLSTM-CRF algorithm combined with Bidirectional Long Short Time Memory(BiLSTM) network and Conditional Random Field(CRF) was applied to implement the extraction of named entities in the personnel performance plan and the realistic text, and the conceptual similarity calculation method based on the Knowledge Network was adopted to calculate the similarity between the same entities, so as to implement the matching calculation between the plan and the actual reality in the personnel performance. Through the experimental analysis of the work plan and realistic text data of the work supervisors in a Railway Administration, the BILSTM-CRF algorithm has an accuracy rate of over 90% for each named entity. The accuracy of personnel planning and realistic matching is 83%. The experiment proves that text computing method based on BiLSTM-CRF and concept similarity is feasible in personnel performance analysis, and can also provide a reference method for similarity calculation of other texts in the railway field.
- text similarity /
- bidirectional long-short time memory network /
- conditional random field /
- named entity recognition /
- conceptual similarity

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参考文献(10)

[1]	史天运,刘军,李平,等. 铁路大数据平台总体方案及关键技术研究[J]. 铁路计算机应用, 2016, 25(9):1-6.
[2]	王喆,马小宁. 铁路大数据应用规划及实施策略研究[J]. 铁路计算机应用, 2018, 27(9):44-47.
[3]	曾云泽. 文本相似度计算方法发展[J]. 数字通信世界, 2018(10):218-218.
[4]	陈二静,姜恩波. 文本相似度计算方法研究综述[J]. 数据分析与知识发现, 2017, 1(6):1-11.
[5]	TurneyP D,Pantel P. From frequency to meaning:vector space models of semantics[J]. Journal of Artificial Intelligence Research, 2010,37(1):141-188.
[6]	GregorK,Danihelka I, Graves A, et al. DRAW:A Recurrent Neural Network For Image Generation[J]. Computer Science, 2015:1462-1471.
[7]	Ju Z, Wang J, Zhu F. Named Entity Recognition from Biomedical Text Using SVM[C]//Bioinformatics and Biomedical Engineering, (iCBBE) 2011 5th International Conference on, 2011:1-4.
[8]	田久乐,赵蔚. 基于同义词词林的词语相似度计算方法[J]. 吉林大学学报(信息科学版), 2010, 28(6):602-608.
[9]	张亮,尹存燕,陈家骏. 基于语义树的中文词语相似度计算与分析[J]. 中文信息学报, 2010, 24(6):23-30.
[10]	朱征宇,孙俊华. 改进的基于《知网》的词汇语义相似度计算[J]. 计算机应用, 2013, 33(8):2276-2279.