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Improved Decomposition Strategy for Joint Entity and Relation Extraction

The joint entity and relation extraction task detects entity pairs along with their relations to extract relational triplets. A recent study (Yu et al. 2020) proposed a novel decomposition strategy that splits the task into two interrelated subtasks: detection of the head-entity (HE) and identification of the corresponding tail-entity and relation (TER) for each extracted head-entity. However, this strategy suffers from two major problems. First, if the HE detection task fails to find a valid head-entity, the model will then miss all related triplets containing this head-entity in the head role. Second, as Yu et al. (2020) stated, their model cannot solve the entity pair overlap (EPO) problem. For a given head-entity, the TER extraction task predicts only a single relation between the head-entity and a tail-entity, even though this entity pair can hold multiple relations. To address these problems, we propose an improved decomposition strategy that considers each extracted entity in two roles (head and tail) and allows a model to predict multiple relations (if any) of an entity pair. In addition, a corresponding model framework is presented to deploy our new decomposition strategy. Experimental results showed that our approach significantly outperformed the previous approach of Yu et al. (2020) and achieved state-of-the-art performance on two benchmark datasets.

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