Abstract
Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies. Existing methods do not fully exploit such dependencies. We present a novel inter-sentence relation extraction model that builds a labelled edge graph convolutional neural network model on a document-level graph. The graph is constructed using various inter- and intra-sentence dependencies to capture local and non-local dependency information. In order to predict the relation of an entity pair, we utilise multi-instance learning with bi-affine pairwise scoring. Experimental results show that our model achieves comparable performance to the state-of-the-art neural models on two biochemistry datasets. Our analysis shows that all the types in the graph are effective for inter-sentence relation extraction.
Abstract (translated)
句间关系提取处理文档中的许多复杂语义关系，这些关系需要局部、非局部、句法和语义依赖性。现有的方法不能充分利用这种依赖性。我们提出了一种新的句子间关系提取模型，该模型在文档级图上建立了一个标记的边缘图卷积神经网络模型。该图是使用不同的句间和句内依赖关系构建的，以捕获本地和非本地依赖关系信息。为了预测一个实体对之间的关系，我们利用双仿射成对得分的多实例学习方法。实验结果表明，该模型在两个生物化学数据集上的性能与最新的神经模型相当。我们的分析表明，图表中的所有类型对于句间关系的提取都是有效的。
URL
https://arxiv.org/abs/1906.04684
