Relation extraction (RE) aims to identify the semantic relations between named entities in text. Recent years have witnessed it raised to the document level, which requires complex reasoning with entities and mentions throughout an entire document. In this paper, we propose a novel model to document-level RE, by encoding the document information in terms of entity global and local representations as well as context relation representations. Entity global representations model the semantic information of all entities in the document, entity local representations aggregate the contextual information of multiple mentions of specific entities, and context relation representations encode the topic information of other relations. Experimental results demonstrate that our model achieves superior performance on two public datasets for document-level RE. It is particularly effective in extracting relations between entities of long distance and having multiple mentions.

中文翻译：

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用于文档级关系提取的全局到局部神经网络

关系提取（RE）旨在识别文本中命名实体之间的语义关系。近年来见证了它上升到文档级别，这需要对整个文档中的实体和提及进行复杂的推理。在本文中，我们提出了一种新的文档级 RE 模型，通过根据实体全局和局部表示以及上下文关系表示对文档信息进行编码。实体全局表示对文档中所有实体的语义信息进行建模，实体局部表示聚合特定实体的多个提及的上下文信息，上下文关系表示对其他关系的主题信息进行编码。实验结果表明，我们的模型在文档级 RE 的两个公共数据集上取得了优异的性能。