Relation Extraction (RE) aims at extracting meaningful relation facts between entities in texts. It is an important semantic processing task in the field of natural language processing (NLP) and has many applications. Traditional RE focuses on extracting entity relationships from a single input sentence. Recently, the research scope has been extended from sentence level to document level. However, compared with sentence-level RE, document-level RE, which needs to identify the inter-sentence relations from entities scattered in different sentences, is more complex and still lacks of solutions. To solve this problem, we propose a novel document-level RE method based on Heterogeneous Graph Neural Networks in this paper. Concretely, to obtain token embeddings containing long-distance dependency signals well, we encode the document with Temporal Convolutional Networks, whose dilated convolution and residual structure allow the effective and efficient preservation of historical information. To better describe the interaction between different elements, we construct the input documents as heterogeneous graphs with different node and edge types and utilize Graph Transformer Networks to generate semantic paths. Numerical experiments on two document-level biomedical datasets demonstrate the effectiveness of the proposed method.

关系提取（RE）旨在提取文本中实体之间有意义的关系事实。它是自然语言处理（NLP）领域中一项重要的语义处理任务，应用广泛。传统的 RE 侧重于从单个输入句子中提取实体关系。最近，研究范围已经从句子级别扩展到文档级别。然而，与句子级 RE 相比，文档级 RE 需要从分散在不同句子中的实体中识别句间关系，更复杂，仍然缺乏解决方案。为了解决这个问题，我们在本文中提出了一种基于异构图神经网络的新型文档级 RE 方法。具体来说，为了得到包含长距离依赖信号的token embeddings，我们使用时间卷积网络对文档进行编码，其扩张卷积和残差结构允许有效且高效地保存历史信息。为了更好地描述不同元素之间的交互，我们将输入文档构建为具有不同节点和边类型的异构图，并利用图转换器网络生成语义路径。在两个文档级生物医学数据集上的数值实验证明了所提出方法的有效性。我们将输入文档构建为具有不同节点和边类型的异构图，并利用图转换器网络生成语义路径。在两个文档级生物医学数据集上的数值实验证明了所提出方法的有效性。我们将输入文档构建为具有不同节点和边类型的异构图，并利用图转换器网络生成语义路径。在两个文档级生物医学数据集上的数值实验证明了所提出方法的有效性。