ETCGN: entity type-constrained graph networks for document-level relation extraction

Document-level relation extraction aims at discerning semantic connections between entities within a given document. Compared with sentence-level relation extraction settings, the complexity of document-level relation extraction lies in necessitating models to exhibit the capability to infer semantic relations across multiple sentences. In this paper, we propose a novel model, named Entity Type-Constrained Graph Network (ETCGN). The proposed model utilizes a graph structure to capture intricate interactions among diverse mentions within the document. Moreover, it aggregates references to the same entity while integrating path-based reasoning mechanisms to deduce relations between entities. Furthermore, we present a novel constraint method that capitalizes on entity types to confine the scope of potential relations. Experimental results on two public dataset (DocRED and HacRED) show that our model outperforms a number of baselines and achieves state-of-the-art performance. Further analysis verifies the effectiveness of type-based constraints and path-based reasoning mechanisms. Our code is available at: https://github.com/yhx30/ETCGN.