基于局部注意力和本地远程监督的实体关系抽取方法

doi:10.13229/j.cnki.jdxbgxb.20230259

Abstract

Abstract:

In order to address the entity relation extraction of overlapping triplets in complex contexts， a joint extraction method called LARE is proposed， which is based on local attention and local remote supervision. First， the pointer hierarchical sequence annotation scheme is designed to solve the problem of entity overlap of triples. Second， a local attention mechanism is proposed， which uses a sliding window to calculate attention to focus on local details. Finally， A knowledge base is established， which is annotated by remote supervision during training， and some entity relationship pairs are randomly replaced to generate new training sentences， so as to enhance the fitting ability of the model. Through comparative experiments，the F₁ value of the proposed method on the Baidu dataset and Thangka dataset reaches 81.49% and 53.07%， which improves the performance of relationship extraction in the case of entity overlap.

Key words: computer application, entity relation extraction, entity overlap, local attention mechanism, local remote supervision

CLC Number:

TP391

Xiao-ran GUO,Tie-jun WANG,Yue YAN. Entity relationship extraction method based on local attention and local remote supervision[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(1): 307-315.

Figures/Tables 8

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参数名称	参数值
DG_CNN层数	12
DG_CNN膨胀率	1，2，5，1，2，5，1，2，5，1，1，1
DG_CNN卷积核大小	3
Local Attention窗口尺寸	3
Local Attention隐层维度	128
BiLSTM隐层维度	128
batch_size	16
初始学习率	0.001
主语首、尾位置阈值	0.3
宾语首、尾位置阈值	0.2

模型名称	P/%	R/%	F₁/%
LARE_-CNN	80.09	76.51	78.26
LARE_-Muti-head	82.32	78.50	80.36
LARE_-LDS	82.56	77.75	80.08
LARE	84.02	79.10	81.49

模型名称	P/%	R/%	F₁/%
LARE_-CNN	77.19	32.07	45.31
LARE_-Muti-head	69.86	36.15	47.65
LARE_-LDS	84.90	37.32	51.85
LARE	88.51	37.90	53.07

模型名称	P/%	R/%	F₁/%
BiLSTM-LSTM	58.46	28.65	38.45
CopyRE	56.37	39.26	46.28
LARE	88.51	37.90	53.07

Entity relationship extraction method based on local attention and local remote supervision

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