  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.
