文献类型：期刊文献

中文题名：基于span指针网络的危险化学品安全知识抽取

英文题名：Safety Knowledge Extraction of Hazardous Chemicals Based on Span Pointer Network

作者：许蒙蒙[1];毛帅[1];唐漾[1];王冰[1]

机构：[1]华东理工大学能源化工过程智能制造教育部重点实验室,上海200237

年份：2022

卷号：29

期号：6

起止页码：1082

中文期刊名：控制工程

外文期刊名：Control Engineering of China

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD_E2021_2022】；

基金：国家科技部重点研发计划项目(2018YFC0809302)。

语种：中文

中文关键词：span指针网络;危险化学品安全知识;关系抽取;Lookahead优化器;对抗训练

外文关键词：Span pointer network;safety knowledge of hazardous chemicals;relation extraction;Lookahead optimizer;adversarial training

摘要：危险化学品安全知识存在数据长、实体跨度大、语义复杂度高且部分实体间存在嵌套等特点,这些特点增大了危险化学品安全知识的抽取难度。为了有效地解决危险化学品安全知识的抽取问题,并针对关系抽取任务中流水线方法存在的误差累积和实体冗余问题,提出了一种基于span指针网络的关系联合抽取方法,旨在抽取危险化学品安全知识中的三元组信息。此外,为了提升模型的抽取性能和泛化能力,在span指针网络的模型基础上,添加了对抗训练和Lookahead优化器。最终的实验结果证明,此方法在解决危险化学品安全知识的抽取问题上具有优越的性能。
Safety knowledge of hazardous chemicals has the characteristics of long data, large entity span, high semantic complexity, and nesting among some entities. These characteristics increase the difficulty of extracting safety knowledge of hazardous chemicals. In order to effectively solve the problem of extracting safety knowledge of hazardous chemicals, and the problems of error accumulation and entity redundancy of the pipeline method in the relation extraction task, a method of relation joint extraction based on span pointer network is proposed in this paper, which aims to extract the triple information in safety knowledge of hazardous chemicals. In addition, in order to improve the extraction performance and generalization ability of the model, adversarial training and Lookahead optimizer are added to the span pointer network model. The final experimental results prove that this method has superior performance in solving the problem of extracting safety knowledge of hazardous chemicals.