基于并行子空间优化的企业命名实体识别  

Enterprise Named Entity Recognition Based on Concurrent Subspace Optimization

作　　者：乔诗展 陈逸伦 QIAO Shi-Zhan;CHEN Yi-Lun(School of Astronautics,Northwestern Polytechnical University,Xi’an 710072,China)

机构地区：[1]西北工业大学航天学院,西安710072

出　　处：《计算机系统应用》2021年第12期262-267,共6页Computer Systems & Applications

摘　　要：针对企业命名实体的识别任务的过程复杂、学科交叉、实时性差等难点,提出了一种基于并行子空间优化的方法.首先,建立系统的目标-约束方程完成系统级优化;其次,再通过构建文字检测、文字识别两级模型,并考虑现存不同模型的优缺点进行模型选择的方法对涉及学科进行并行优化;随后,再使用图像阈值、灰度化、霍夫变换等算法构建两级模型的衔接;最后,通过仿真实验,验证了本文方法相比其他两级文字检测识别模型的识别准确率提高了9%,推理速度提升约20%.Concerning the complicated process,interdisciplinarity,and poor real-time performance of enterprise named entity recognition,a method based on concurrent subspace optimization is proposed.First,a target-constrained equation of the system is established to complete system-level optimization;secondly,a two-level model of text detection and recognition is constructed,and the model is selected,considering the advantages and disadvantages of different existing models,to optimize the discipline in parallel;then,the connection of the two-level model is constructed with the image threshold,grayscale and Hoff transform;finally,simulation experiments verify that the recognition accuracy of this method is 9%higher than that of other two-level text detection and recognition models,and the speed increases by about20%.

关 键 词：命名实体识别 文字检测 文字识别 并行子空间优化 

分 类 号：F272[经济管理—企业管理] TP391.41[经济管理—国民经济]