Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (3): 97-106.doi: 10.11896/jsjkx.250500095

• Intelligent Information System Based on AGI Technology • Previous Articles     Next Articles

Joint Entity and Relation Extraction Method with Multi-scale Collaborative Aggregation and Axial-semantic Guidance

QIAN Qing1,3, CHEN Huicheng1, CUI Yunhe2, TANG Ruixue1,3, FU Jinmei1   

  1. 1 School of Information, Guizhou University of Finance and Economics, Guiyang 550025, China
    2 Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, College of Computer Science & Technology, Guizhou University, Guiyang 550025, China
    3 Guizhou Provincial Key Laboratory of Computing and Network Convergence, Guiyang 550025, China
  • Received:2025-05-22 Revised:2025-09-12 Published:2026-03-12
  • About author:QIAN Qing,born in 1986,Ph.D,asso-ciate professor,is a member of CCF(No.K8203S).Her main research interests include natural language processing,digital media forensics and multimedia signal processing.
    CUl Yunhe,born in 1987,Ph.D,asso-ciate professor,is a member of CCF(No.F3600M).His main research interests include lightweight large mo-dels,network security,software-defined networks,data center networks and network telemetry.
  • Supported by:
    National Natural Science Foundation of China(62462010,61902085) and Guizhou Provincial Science and Technology Projects(QKH-Basic-ZK[2022]General 018).

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Abstract: In recent years,table-filling approaches to joint entity-relation extraction have achieved impressive performance,yet they typically neglect two critical challenges:modelling boundary correlations among token pairs and distinguishing semantically similar token pairs.To address these gaps,this paper introduces a novel joint extraction model featuring multi-scale semantic aggregation and axial-semantic guidance.Firstly,multi-scale semantic aggregation module applies parallel depthwise convolutions of varying kernel sizes to capture boundary correlation information across multiple spatial arrangements,thereby enriching token-pair representations and facilitating the detection of implicit entities.Next,axial-semantic guidance module employsrow-and co-lumn-wise banded convolutions to perform axis-aligned attention calibration,strengthening key semantic features and effectively resolving high-similarity ambiguities.Comprehensive experiments on NYT*,WebNLG*,NYT,and WebNLG datasets yield F1 scores of 93.2%,94.5%,93.2%,and 91.4%－corresponding to absolute gains of 0.1 percentage points,0.6 percentage points,0.4 percentage points,and 1.0 percentage points over strong baselines.These results validate that explicitly capturing multi-scale boundary correlations and refining semantic alignment substantially enhances joint entity-relation extraction.

Key words: Natural language processing, Joint entity-relation extraction, Multi-scale semantic aggregation, Axial semantic gui-dance, Convolutional attention mechanism

CLC Number: 

  • TP391
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