Boundary assembling method for joint entity and relation extraction

Abstract

In recent years, a paradigm shift has occurred in the field of joint entity and relation extraction from token tagging to span classification, because the latter can handle nested named entities in a sentence and better utilize the global features of a possible named entity. Because relation extraction should verify every entity pair in a sentence, the performance of joint entity and relation extraction significantly depends on the quality of the entity span proposals. Most models enumerate numerous inaccurate entity spans, causing a severe data imbalance problem and high computational complexity. To address these problems, we propose a boundary assembling model for joint entity and relation extraction, in which entity boundaries are assembled to enumerate entity spans. Entity boundaries have small granularity and are less ambiguous, hence, the proposed model can benefit from accurate entity spans. Furthermore, boundary detection, span classification, and relation extraction are integrated into an end-to-end framework; thus, our model can share model parameters for multi-objective learning, which enhances the discriminability of a neural network. Experiments show that our boundary assembling model outperforms existing state-of-the-art models on four evaluation datasets: SciERC, ADE, ACE05, and CoNLL04.