基于文本挖掘技术的铁路安监人员履职分析

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。

Performance analysis of railway safety supervisor based on text mining technology

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.