基于深度学习的实体关系抽取方法文献综述

易 战军, 曾 锐
宜春学院人工智能与信息工程学院

摘要


实体关系抽取作为自然语言处理（NLP）领域信息抽取的核心任务，在知识图谱构建、智能问答等场景中
发挥关键作用，其效率与准确性直接影响海量非结构化文本的价值挖掘。深度学习凭借自主特征学习能力成为实体
关系抽取主流技术，递归神经网络（RNN）及其变体（LSTM、Bi-LSTM）通过时序建模解决长距离依赖问题，但
存在参数训练复杂或训练周期长等不足；卷积神经网络（CNN）利用卷积与池化操作高效提取局部语义特征，结合
位置特征等改进进一步提升性能，却在长句建模中存在局限；图神经网络（GNN）通过图结构与节点交互优化全局
关系建模，在文档级实体关系抽取中优势显著，其优化聚焦依存树剪枝、多模态图表示等方向；联合学习通过参数
共享或序列标注实现端到端建模，部分方法针对特定领域优化特征融合，提升抽取鲁棒性；大语言模型（LLM）借
助大规模预训练与微调增强语义理解和泛化能力，在低资源、零样本场景及专业术语处理中表现突出，参数高效微
调等技术进一步优化其性能。

关键词


实体关系抽取；RNN；LSTM；CNN；GNN；LLM

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参考


[1]Socher R, Huval B, Manning C D, et al. Semantic

compositionality through recursive matrix-vector spaces[C]//

Proc of Empirical Methods in Natural Language Processing.

2012: 1201-1211.

[ 2 ] N I N G T H O U J A M D , Y A D A V S ,

BHATTACHARYYA P, et al. Relation extraction between

the clinical entities based on the shortest dependency path

based lstm[J]. arXiv: 1903. 09941, 2019.

[3]Liu Chunyang, Sun Wenbo, Chao Wenlan, et al.

Convolution neural network for relation extraction[C]//

Advanced Data Mining and Applications. 2013: 231-242.

[4]ZENG D, LIU K, LAI S, et al. Relation classification

via convolutional deep neural network[C]//Proceedings

of the25th International Conference on Computational

Linguistics: Technical Papers, 2014: 2335-2344.

[5]NGUYEN T H, GRISHMAN R. Relation extraction:

perspective from convolutional neural networks[C]//Proceedings

of the 1st Workshop on Vector Space Modeling for Natural

Language Processing, 2015: 39-48.

[6]SONG L, ZHANG Y, GILDEA D, et al. Leveraging

dependency forest for neural medical relation extraction[J].

arXiv: 1911. 04123, 2019.

[7]FU T J, LI P H, MA W Y. GraphRel: modeling text

as relational graphs for joint entity and relation extraction[C]//

Proceedings of the 57th Annual Meeting of the Association

for Computational Linguistics, 2019: 1409-1418.

[8]Miwa M, Bansal M. End-to-end relation extraction

using LSTMs on sequences and tree structures[C]//Proc of

Meeting of the Association for Computational Linguistics.

2016: 1105-1116.

[9]Zheng Suncong, Wang Feng, Bao Hongyun, et al.

Joint extraction of entities and relations based on a novel

tagging scheme[C]//Proc of Meeting of the Association for

Computational Linguistics. 2017: 1227-1236.

[10]张西硕，柳林，王海龙，等. 知识图谱中实体关

系抽取方法研究[J]. 计算机科学与探索，2024，18（03）：

574-596.

[11]WANG J X, ZHANG L L, LEE W S, et al. When

phrases meet probabilities: enabling open relation extraction

with cooperating large language models[C]//Proceedings of

the62nd Annual Meeting of the Association for Computational

Linguistics. Stroudsburg: ACL, 2024: 13130-13147.


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