BSLRel:基于二元序列标注的级联关系三元组抽取模型  

BSLRel:A Binary Sequence Labeling Based Cascading Relation Triple Extraction Model

作　　者：张龙辉 尹淑娟 任飞亮 苏剑林 明瑞成 白宇佳 ZHANG Longhui;YIN Shujuan;REN Feiliang;SU Jianlin;MING Ruicheng;BAI Yujia(School of Computer Science and Engineering,Northeastern University,Shenyang,Liaoning 110819,China;Shenzhen Zhuiyi Technology Co.Ltd,Shenzhen,Guangdong 518057,China)

机构地区：[1]东北大学计算机科学与工程学院,辽宁沈阳110819 [2]深圳追一科技有限公司,广东深圳518057

出　　处：《中文信息学报》2021年第6期74-84,共11页Journal of Chinese Information Processing

基　　金：国家重点研发计划项目(2018YFC0830701);国家自然科学基金(61572120);中央高校基本科研业务专项资金(N181602013)。

摘　　要：关系三元组抽取是构建大规模知识图谱的基础,近年来受到学术界和工业界的广泛关注。为了提高模型对重叠关系三元组和多槽值关系三元组的抽取能力,该文提出了一个基于神经网络的端到端的关系三元组抽取模型BSLRel。其主要特点是将关系三元组抽取任务转化为级联的二元序列标注任务,并使用多信息融合结构Conditional Layer Normalization进行信息融合。实验结果显示,BSLRel模型对重叠关系三元组和多槽值关系三元组具有较强的抽取能力。基于BSLRel模型,该团队参加了“2020语言与智能技术竞赛”中的关系三元组抽取任务,并取得了第五名的成绩。Extracting relational triples is a basic task for large-scale knowledge graph construction.In order to improve the ability of extracting overlapped relation triples and multi-slot relation triples,this paper proposes BSLRel,an end-to-end relation triple extraction model based on neural network.Specifically,BSLRel model converts the relation triplet extraction task into a cascade binary sequence labeling task,which consists of a new multiple information fusion structure“Conditional Layer Normalization”to integrate information.With BSLRel,we participate in the“Relation Extraction”task organized by“the 2020 Language and Intelligence Challenge”and achieve Top 5 among all competitive models.

关 键 词：BSLRel模型 重叠关系三元组抽取 多槽值关系三元组抽取 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]