论文标题
用于文档级关系提取的全球到本地神经网络
Global-to-Local Neural Networks for Document-Level Relation Extraction
论文作者
论文摘要
关系提取（RE）旨在确定文本中指定实体之间的语义关系。近年来，它已将其提高到文档级别，这需要在整个文档中与实体和提及的复杂推理。在本文中，我们通过根据实体全球和本地表示以及上下文关系表示来编码文档信息来提出一个新颖的模型来文档级别的重复。实体全局表示文档中所有实体的语义信息，实体本地表示汇总了特定实体的多个提及的上下文信息，而上下文关系表示表示其他关系的主题信息。实验结果表明，我们的模型在两个用于文档级RE的公共数据集上实现了卓越的性能。它在提取长距离实体和多个提及之间的关系方面特别有效。
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.
