中国科学院大学学报2022，Vol.39Issue(2)：240-251,12.DOI:10.7523/j.ucas.2020.0026
一种新的基于深度学习的重叠关系联合抽取模型
A new joint model for extracting overlapping relations based on deep learning
摘要
Abstract
With the rapid developments of Internet technologies and popularization of Internet among daily activities,we are surrounded by all kinds of information every moment.Hence,to mine valuable information from massive data has always been a hotspot of research at home and abroad.In this environment,relationship extraction is an important subtask of information extraction,which purpose is to identify the relationship between entities from the text,so as to mine the structured information in the text,that is,fact triplet.In the text,entity overlapping and relationship overlapping are very common phenomena,but the existing joint extraction model cannot effectively solve such problems,so the paper proposes a new joint extraction model,which regards the relationship extraction task as consisting of entity recognition and relationship recognition of two subtasks.The two subtasks are identified using sequence labeling method and multi-classification method,respectively.In the joint extraction process,in order to fully mine the semantic information of the text,the part of speech(POS)and syntactic dependency(Deprel)features were added to the input layer of the model.Attention mechanism is also introduced in the model,which can eliminate the problem of long-distance dependence as sentence length increases.Finally,the paper conducts relationship extraction experiments on the NYT dataset and the WebNLG dataset.The experimental results show that the model proposed in the paper can effectively solve the problem of overlapping relationships and obtain the best extraction effect.关键词
关系抽取/实体重叠/联合抽取模型/深度学习Key words
relation extraction/entity overlapped/joint extraction model/deep learning分类
信息技术与安全科学引用本文复制引用
赵敏钧,赵亚伟,赵雅捷,罗刚..一种新的基于深度学习的重叠关系联合抽取模型[J].中国科学院大学学报,2022,39(2):240-251,12.基金项目
Supported by the National Natural Science Foundation of China(61872331)and University of Chinese Academy of Sciences （61872331）
