Multi-Stream Semantics-Guided Dynamic Aggregation Graph Convolution Networks to Extract Overlapping Relations

The existing relation extraction approaches select the relevant partial dependency structures and exhibit the limitation associated with long-distance dependencies. Moreover, due to the repeated use of the irrelevant redundant information and the lack of consideration of the key semantic details, the extraction of relations is relatively complex when the entities overlap. To address this limitation and effectively exploit the relevant information while ignoring irrelevant information, this paper proposes a simple but effective multistream semantics-guided dynamic aggregation graph convolution network (SG-DAGCN) to realize the extraction of overlapping relations. The proposed model constructs the entity relation graphs by enumerating the possible candidates and external auxiliary information and adaptively manages the relevant substructure. Subsequently, this framework models the relational graphs between the entities through a dynamic aggregation graph convolution module and gradually produces the discriminative embedded features and a refined graph through the dynamic aggregation of nodes. The proposed approach can effectively leverage the rich multiscale structural information and capture the long-distance dependencies between overlapping entities in long sentences. The results of the experiments conducted on two typical benchmark datasets show that the proposed model can achieve a high level of performance and outperform other state-of-the-art methods in both qualitative and quantitative aspects.