深度语句级实体关系抽取综述

doi:10.19665/j.issn1001-2400.20240311

Abstract

Abstract:

Entity relation extraction at statement level(RE) refers to the extraction of semantic relationship between an entity pair from a given statement.It is an important basis for the construction of knowledge graph,natural language processing(NLP),intelligent question answering,Web search and other applications in artificial intelligence(AI),and it is the most cutting-edge basic hot research issue in AI.With the successful application of deep neural networks(DNNs),a variety of RE algorithms based on DNNs have emerged.In recent years,with the requirement of continuous processing and understanding of text information,some deep continuous of entity relation extraction(CRE) algorithms by combining entity relationship extraction and continual learning(CL) have emerged.This kind of algorithms can enable the model to carry out sequential RE of multiple tasks sustainably and efficiently without forgetting the learned knowledge of old tasks.In this paper,various representative deep RE and CRE methods in recent years are surveyed from their deep network model,algorithm framework and performance characteristics,and the research development trends of the RE and CRE are pointed out.We sincerely hope that the extensive survey will inspire more good ideas on the research of the RE and CRE.

Key words: deep learning, natural language processing, entity relation extraction(RE), continuous learning(CL), continuous entity relation extraction(CRE)

CLC Number:

TP183

ZHAO Congjian, JIAO Yiyuan, LI Yanni. Overview of deep sentence-level entity relation extraction[J].Journal of Xidian University, 2024, 51(6): 117-131.

Figures/Tables 11

References 89

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分类	算法	主干网络模型	损失函数	分类性能
CNN/RNN-based	DepLCNN^[35]2015)	CNN	交叉熵损失函数	较低
	BRCNN^[36](2016)	CNN	双向交叉熵损失函数	较低
	ATBLSTM^[37]2018)	LSTM	交叉熵损失函数	较低
	REC^[40](2023)	BiLSTM	交叉熵损失函数	中等
GCN/GNN-based	GraphCache^[47](2022)	BERT	交叉熵损失函数	中等
	ERECKG^[54](2021)	BERT	双目标的交叉熵损失和三元组损失	中等
	RE²^[52](2023)	BERT	交叉熵损失函数	较高
	TS-GCN^[53](2023)	BERT	交叉熵损失函数	较高
PTL-based	BERTEM^[55](2019)	BERT	优化的交叉熵损失函数	较高
	R-BERT^[56](2019)	BERT	交叉熵损失函数	较高
	CP^[57](2020)	BERT	双目标的三元组损失和掩码语言建模损失	较高
Knowledge-based	Student-R^[60](2020)	BiLSTM	双目标最高分数差损失和知识蒸馏损失	较高
	RECON^[62](2021)	BiLSTM+CNN	交叉熵损失函数	较高
	KnowPrompt^[63](2022)	BERT	双目标的隐式结构化约束损失和掩码损失	较高
	PTCAS^[65](2024)	BERT	双目标知识结构化约束损失和掩码语言建模损失	较高

算法	主干网络模型	CL中抵抗CF的主要机制	抵抗遗忘能力	算法分类性能
EA-EMR^[5](2019)	BiLSTM^[48]	不同任务的语义表示嵌入/表示空间对齐	较低	较低
MLLRE^[67](2019)	BiLSTM	元学习机制	较低	较低
EMAR^[68](2020)	BiLSTM	关系原型+回放	中等	较低
ELRE^[69](2020)	BiLSTM	正则化+回放	中等	较低
RP-CRE^[70](2021)	BERT^[38]	精化原型样本表示+回放	中等	中等
CML^[71](2021)	BiLSTM	课程学习+元学习	中等	中等
CEMR^[72](2021)	BiLSTM	有效样本选择与替换+回放	较高	中等
CRL^[73](2022)	BERT	对比学习+知识蒸馏	较高	较高
CRECL^[74](2022)	BERT	分类网络+原型对比网络	较高	较高
KIP-Frame^[75](2022)	Prompt	利用注意力机制求更好的原型+回放	较高	较高
ACA^[81](2022)	BERT	数据增强+回放	较高	较高
MRMR^[80](2023)	BiLSTM	三阶段的学习机制+回放	较高	较高
CRL-protoAug^[82](2022)	BERT	一致表示学习与原型增强+回放	较高	较高
ConPL^[83](2023)	BERT	样本和原型一致性分布+回放	较高	较高
SCKD^[84](2023)	BERT	序列知识蒸馏与对比学习+回放	较高	较高
CDec^[85](2023)	BERT	分类器分解+回放	较高	较高
CEAR^[86](2023)	BERT	记忆不敏感原型与记忆增强+回放	较高	较高
FDCRE^[87](2023)	BERT	逐类正则化+回放	较高	较高
BFP^[88](2023)	BERT	后向投影+长短期记忆+回放	较高	较高

Overview of deep sentence-level entity relation extraction

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[11]	LIU Zhenyan, ZHANG Hua, LIU Yong, YANG Libo, WANG Mengdi. Efficient seed generation method for software fuzzing [J]. Journal of Xidian University, 2024, 51(2): 126-136.
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