Existing models extract entity relations only after two entity spans have been precisely extracted that influenced the performance of relation extraction. Compared with recognizing entity spans, because the boundary has a small granularity and a less ambiguity, it can be detected precisely and incorporated to learn better representation. Motivated by the strengths of boundary, we propose a boundary determined neural (BDN) model, which leverages boundaries as task-related cues to predict the relation labels. Our model can predict high-quality relation instance via the pairs of boundaries, which can relieve error propagation problem. Moreover, our model fuses with boundary-relevant information encoding to represent distributed representation to improve the ability of capturing semantic and dependency information, which can increase the discriminability of neural network. Experiments show that our model achieves state-of-the-art performances on ACE05 corpus.

中文翻译：

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用于关系提取的边界确定神经模型

现有模型只有在两个实体跨度被精确提取后才提取实体关系，这影响了关系提取的性能。与识别实体跨度相比，由于边界的粒度小，模糊性更小，因此可以精确检测并合并以学习更好的表示。受边界优势的启发，我们提出了一种边界确定神经 (BDN) 模型，该模型利用边界作为与任务相关的线索来预测关系标签。我们的模型可以通过边界对预测高质量的关系实例，这可以缓解错误传播问题。此外，我们的模型融合了边界相关信息编码来表示分布式表示，以提高捕获语义和依赖信息的能力，这可以增加神经网络的可辨别性。实验表明，我们的模型在 ACE05 语料库上达到了最先进的性能。