摘要
[目的/意义]为解决目前网络公开平台的多源异构的企业数据的散乱、无序、碎片化问题,提出Bi-LSTM-CRF深度学习模型进行商业领域中的命名实体识别工作。[方法/过程]该方法包括对企业全称实体、企业简称实体与人名实体3类命名实体识别。[结果/结论]实验结果显示对企业全称实体、企业简称实体与人名实体3类命名实体识别的识别率平均F值为90.85%,验证了所提方法的有效性,证明了本研究有效地改善了商业领域中的命名实体识别效率。
[Purpose/Significance]In order to solve the problem of scattered,disordered and fragmented multi-source heterogeneous enterprise data of the current network open platform,the Bi-LSTM-CRF deep learning model was proposed for the named entities recognition in the business field.[Method/Process]This method included three kinds of named entities:enterprise full name entity,enterprise short name entity and personal name entity.[Result/Conclusion]The experimental results showed that the average F value of the recognition rate of the three types of named entities,namely enterprise full entity,enterprise abbreviation entity and person name entity,was 90.85%,which verified the effectiveness of the proposed method.It was proved that this study effectively improved the efficiency of named entity recognition in the commercial field.
作者
丁晟春
方振
王楠
Ding Shengchun;Fang Zhen;Wang Nan(School of Economics and Management,Nanjing University of Science and Technology,Nanjing 210094,China;Jiangsu Social Public Security Science and Technology Collaborative Innovation Center,Nanjing 210094,China)
出处
《现代情报》
CSSCI
2020年第3期103-110,共8页
Journal of Modern Information
基金
国家社会科学基金项目“基于社会网络分析的网络舆情主题发现研究”(项目编号:15BTQ063)
