Global-to-Local Neural Networks for Document-Level Relation Extraction

TL;DR

This paper introduces a novel neural network model for document-level relation extraction that effectively captures global and local entity information and context relations, leading to improved performance on public datasets.

Contribution

The paper presents a new model that encodes document information through entity global/local and context relation representations for better relation extraction.

Findings

Achieves superior performance on two public datasets.

Effective in extracting relations between distant entities.

Handles multiple mentions of entities accurately.

Abstract

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.