Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (12): 4045-4051.doi: 10.13229/j.cnki.jdxbgxb.20240474

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Entity relationship extraction method based on span and semantic features

Ping FENG1,2(),Zi-qian YANG1,Ren-jie WANG1,Shi-yu FENG1,Hang WU1,Yu SUN3   

  1. 1.College of Computer Science and Technology，Changchun University，Changchun 130022，China
    2.Ministry of Education Key Laboratory of Intelligent Rehabilitation and Barrier-free Access for the Disabled，Changchun University，Changchun 130022，China
    3.College of Special Education，Changchun University，Changchun 130022，China
  • Received:2024-04-30 Online:2025-12-01 Published:2026-02-03

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Abstract:

A end-to-end entity relationship extraction model based on span and semantic features was proposed to address the problems of traditional entity relationship extraction methods relying on distance measurement or simple span recognition， which make it difficult to capture potential relationships between entities， and the high computational complexity and error propagation of the model. Firstly， the text vectors were randomly segmented into span sequences so that the model can learn a wider range of semantic feature information. Secondly， semantic relationships were judged to screen out subsets of candidate relationships， thus reducing information redundancy. Finally， the candidate relationships were transformed into relationship-span combinations containing important relationship semantics， and the Transformer decoder was used to achieve the joint extraction of entity relationships. The experimental results show that the F1 value of this model is significantly improved in the NYT and WebNLG datasets compared to other baseline models， proving its effectiveness.

Key words: entity relationship extraction, span, semantic feature, context information

CLC Number: 

  • TP391

Fig.1

Semantic-SPAN entity relation extraction model"

Fig.2

Semantic relationship determines the module structure"

Fig.3

Structure of joint extraction module"

Table 1

Experimental environment setting"

参数值
GPU/（24 G）4 090
内存/G32
batch size16
学习率0.000 1
max span6
epoch100
BERT_emb768

Table 2

Experimental results of model comparison"

模型NYTWebNLG
PRF1PRF1
CopyRE61.056.658.737.736.437.1
GraphRel63.960.061.944.741.142.9
PRGC89.682.385.890.688.589.5
GRTE92.993.193.093.794.293.9
Ours93.193.893.494.094.194.0

Fig.4

Loss change curve （compared to the GRTE model）"

Table 3

Results of ablation experiments of the proposed model on two datasets"

数据集模式F1/%
NYT语义筛选关系子集93.4
常规90.3
WebNLG语义筛选关系子集94.0
常规88.7

Fig.5

Change curves of F1 values for different Dropout parameters"

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