Enhancing Entity Relationship Extraction in Dialogue Texts Using Hypergraph and Heterogeneous Graph  

作　　者：Shunmiao Zhang Siyuan Zheng Degen Huang Dan Li 

机构地区：[1]School of Computer Science and Mathematics,Fujian University of Technology,Fuzhou 350118,China [2]Fujian Provincial Key Laboratory of Big Data Mining and Applications,Fujian University of Technology,Fuzhou 350118,China [3]School of Computer Science and Technology,Dalian University of Technology,Dalian 116024,China [4]Elsevier,Amsterdam 1043NX,The Netherlands

出　　处：《Chinese Journal of Electronics》2025年第1期295-308,共14页电子学报(英文版)

基　　金：supported by the National Key Research and Development Program of China(Grant No.2020AAA0108004);the Fujian Province Science and Technology Guiding Project(Grant No.2022H0025)。

摘　　要：Dialogue-based relation extraction(DialogRE)aims to predict relationships between two entities in dialogue.Current approaches to dialogue relationship extraction grapple with long-distance entity relationships in dialogue data as well as complex entity relationships,such as a single entity with multiple types of connections.To address these issues,this paper presents a novel approach for dialogue relationship extraction termed the hypergraphs and heterogeneous graphs model(HG2G).This model introduces a two-tiered structure,comprising dialogue hypergraphs and dialogue heterogeneous graphs,to address the shortcomings of existing methods.The dialogue hypergraph establishes connections between similar nodes using hyper-edges and utilizes hypergraph convolution to capture multi-level features.Simultaneously,the dialogue heterogeneous graph connects nodes and edges of different types,employing heterogeneous graph convolution to aggregate cross-sentence information.Ultimately,the integrated nodes from both graphs capture the semantic nuances inherent in dialogue.Experimental results on the DialogRE dataset demonstrate that the HG2G model outperforms existing state-of-the-art methods.

关 键 词：Dialogue relation extraction Hypergraph convolution Heterogeneous graph convolution 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]