摘要
命名实体识别是一种有效的设备运行日志分析方法,不仅提高了故障检测的准确度,而且为智能运维策略的优化提供了强有力的支持。鉴于设备运行日志的专业性和复杂性,提出一种基于机器阅读理解的设备故障命名实体识别方法,该方法通过将特定的实体类别转化为自然语言查询,并将实体类别信息融合到这些查询中,有效地克服了传统方法在标签语义信息上的不足,并在实体边界定位的准确性上取得了显著提升。实验表明该方法在设备故障命名实体识别的准确性和有效性方面明显优于现有的基线方法。
Named entity recognition plays a crucial role in analyzing equipment operation logs,not only enhancing the accuracy of fault detection but also providing strong support for the optimization of maintenance strategies.Given the professionalism and diversity of equipment operation logs,this paper proposes a fault entity recognition method based on machine reading comprehension.This method transformed specific entity categories into natural language queries and integrated entity category information into these queries,which effectively overcame the deficiencies of traditional methods in label semantic information and significantly improved the accuracy of entity boundary positioning.Experiments conducted on a dataset for equipment fault entity recognition show that this method excels in accurately identifying equipment fault entities,significantly outperforming existing baseline methods.
作者
徐鹏
龚伟
宋俊典
Xu Peng;Gong Wei;Song Jundian(The Third Research Institute of the Ministry of Public Security,Shanghai 200031,China;Shanghai Metro First Operation Co.,Ltd.,Shanghai 200003,China;Shanghai Development Center of Computer Software Technology,Shanghai 200112,China)
出处
《计算机应用与软件》
北大核心
2024年第5期171-176,273,共7页
Computer Applications and Software
关键词
机器阅读理解
命名实体识别
设备故障
Machine reading comprehension
Named entity recognition
Equipment fault
