摘要
【目的】为有效抽取典籍中蕴含的事件信息,构建面向典籍的事件抽取框架,并采用RoBERTa-CRF模型实现事件类型、论元角色和论元的抽取。【方法】选择《左传》的战争句作为实验数据,建立事件类型和论元角色的分类模板。基于RoBERTa-CRF模型,先用多层Transformer提取语料特征,再结合前后文序列标签学习相关性约束,由输出的标记序列识别论元并对其进行抽取。【结果】对比GuwenBERT-LSTM、BERT-LSTM、RoBERTa-LSTM、BERT-CRF、RoBERTa-CRF等5种模型在数据集上的事件抽取实验结果,RoBERTa-CRF的精确度为87.6%、召回率为77.2%、F1值达到82.1%,验证了该模型的有效性和可操作性。【局限】使用的数据集规模较小,无法使主题类别更均衡化。【结论】本文构建的RoBERTa-CRF模型提升了面向《左传》战争句的事件抽取效果。
[Objective]This paper constructs a framework to extract events from ancient books,which uses the RoBERTa-CRF model to identify event types,argument roles and arguments.[Methods]We collected the war sentences from Zuozhuan as the experimental data,which helped us establish the classification schema for event types and argument roles.Based on the RoBERTa-CRF model,we used the multi-layer transformer to extract the corpus features,which were combined with the sequence tags to learn the correlation constraints.Finally,we identified and extracted the arguments by the tag sequence.[Results]The accuracy,recall and F1 values of the proposed model were 87.6%,77.2%and 82.1%,which were higher than results of the GuwenBERT-LSTM,BertLSTM,RoBERTa-LSTM,Bert-CRF and RoBERTa-CRF on the same dataset.[Limitations]The size of the experimental dataset needs to be expanded,which could make the topic categories more balanced.[Conclusions]The RoBERTa-CRF model constructed in this paper could effectively extract events from ancient Chinese books.
作者
喻雪寒
何琳
徐健
Yu Xuehan;He Lin;Xu Jian(College of Information Management,Nanjing Agricultural University,Nanjing 210095,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2021年第7期26-35,共10页
Data Analysis and Knowledge Discovery
基金
南京农业大学中央高校基本科研业务费(项目编号:SKCX2020006)
中国博士后面上基金(项目编号:2020M681652)的研究成果之一。
