Shunmiao ZHANG, Siyuan ZHENG, Degen HUANG, et al., “Enhancing Entity Relationship Extraction in Dialogue Texts using Hypergraph and Heterogeneous Graph,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–14, xxxx. DOI: 10.23919/cje.2023.00.315
Citation: Shunmiao ZHANG, Siyuan ZHENG, Degen HUANG, et al., “Enhancing Entity Relationship Extraction in Dialogue Texts using Hypergraph and Heterogeneous Graph,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–14, xxxx. DOI: 10.23919/cje.2023.00.315

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

  • Dialogue relationship extraction (RE) 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.
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