基于复杂网络的招聘文本挖掘研究——以互联网金融招聘数据为例

Recruitment Text Mining Based on Complex Network Technology: Case Study of Internet Financial Recruitment Data

[目的/意义]旨在为高校人才培养、人才就业等有关部门提供参考。[方法/过程]用自然语言处理方法对中文招聘文本分词并构建岗位、技能词典,提取文本中岗位-技能实体并构建网络模型;利用复杂网络工具对网络可视化并从网络角度解读岗位-技能关系。[结果/结论]复杂网络可以挖掘文本信息中的内在知识关联,提高网络信息资源利用率。

[Purpose/significance]The paper is to provide references for relevant departments of university talent training and talent employment.[Method/process]The paper uses the natural language processing(NLP)method to realize Chinese recruitment text segmentation,build a position and a skill dictionary,extract the position-skill entity in texts and construct a network model;the network is visualized by the complex network tool and the post-skill relationship is interpreted from the network angle.[Result/conclusion]Complex network method can mine the inherent knowledge correlation structure in text information and improve the utilization rate of network information resources.