一种基于文本挖掘的铁路基础设施设备风险隐患识别模型
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摘要
提出一种基于文本挖掘的铁路基础设施设备风险隐患识别模型,该模型采用基于层叠隐马尔科夫的分词算法对长文本形式的设备质量问题数据进行分词处理,在此基础上,统计每类词出现的频度,识别铁路基础设施设备管理风险隐患,利用词云图可视化技术,对分析结果进行直观、清晰地展示。作者选取了兰州铁路局2012年1月~2016年4月期间4 662条工务、电务和供电专业的铁路基础设施设备质量问题数据,验证了模型的有效性。
A text mining based identification model for railway infrastructure risk was proposed in this paper. The model used segmentation algorithm based on Cascaded Hidden Markov Model(CHMM) to deal with data in the form of long text, which recorded railway infrastructure quality problems. Then, the word frequency was calculated and the railway infrastructure management risk was identified. The analysis result was intuitively and clearly displayed by using the visualization technology of word cloud. The proposed model was experimentally verified by using 4 662 records of railway infrastructure quality problems in Lanzhou Railway Administration between January 2012 to April 2016.
A text mining based identification model for railway infrastructure risk was proposed in this paper. The model used segmentation algorithm based on Cascaded Hidden Markov Model(CHMM) to deal with data in the form of long text, which recorded railway infrastructure quality problems. Then, the word frequency was calculated and the railway infrastructure management risk was identified. The analysis result was intuitively and clearly displayed by using the visualization technology of word cloud. The proposed model was experimentally verified by using 4 662 records of railway infrastructure quality problems in Lanzhou Railway Administration between January 2012 to April 2016.
引文
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[6]杨张博,高山行.基于文本挖掘和语义网络方法的战略导向交互现象研究-以生物技术企业为例[J].科学学与科学技术管理,2015(1):139-150.
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[8]刘群,张华平,俞鸿魁,等.基于层叠隐马模型的汉语词法分析[J].计算机研究与发展,2004(8):1421-1429.
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[12]R Core Team.R:A Language and Environment for Statistical Computing[EB/OL].[2014-04-09].http://www.R-project.org/.
[2]陈勇跃,田文芳,吴金红.主题领域研究热点跟踪及趋势预测的可视化分析方法研究[J].情报理论与实践,2017(6):117-121.
[3]陈苗,刘超,庄俊玲,等.基于文本挖掘的临床带教评价分析[J].中国卫生统计,2017(1):59-60.
[4]史玉珍,吕琼帅.基于进化模糊规则的Web新闻文本挖掘与分类方法[J].湘潭大学自然科学学报,2016,38(2):99-103.
[5]蔡溢,杨洋,殷红梅.基于ROST文本挖掘软件的贵阳市城市旅游品牌受众感知研究[J].重庆师范大学学报:自然科学版,2015(1):126-134.
[6]杨张博,高山行.基于文本挖掘和语义网络方法的战略导向交互现象研究-以生物技术企业为例[J].科学学与科学技术管理,2015(1):139-150.
[7]韩冬煦,常宝宝.中文分词模型的领域适应性方法[J].计算机学报,2015,38(2):272-281.
[8]刘群,张华平,俞鸿魁,等.基于层叠隐马模型的汉语词法分析[J].计算机研究与发展,2004(8):1421-1429.
[9]Viterbi A J.Error bounds for convolutional codes and an anasymptotically optimumdecoding algorithm[J].IEEE ETransactions on Information Theory,1967,13(2):260-269.
[10]郭曙纶.汉语语料库应用教程[M].上海:上海交通大学出版社,2013.
[11]Afzal S,Maciejewski R,Yun J,et al.Spatial Text Visualization Using Automatic Typographic Maps[J].IEEE Transactions on Visualization&Computer Graphics,2012,18(12):2556-2564.
[12]R Core Team.R:A Language and Environment for Statistical Computing[EB/OL].[2014-04-09].http://www.R-project.org/.