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。
Abstract
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
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Key words
BSLRel /
overlapped relation triple /
multi-slot relation triple
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参考文献
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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)
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