Distantly supervised datasets for relation extraction mostly focus on sentence-level extraction, and they cover very few relations. In this work, we propose cross-document relation extraction, where the two entities of a relation tuple appear in two different documents that are connected via a chain of common entities. Following this idea, we create a dataset for two-hop relation extraction, where each chain contains exactly two documents. Our proposed dataset covers a higher number of relations than the publicly available sentence-level datasets. We also propose a hierarchical entity graph convolutional network (HEGCN) model for this task that improves performance by 1.1\% F1 score on our two-hop relation extraction dataset, compared to some strong neural baselines.
@article{arxiv.2108.09505,
title = {A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents},
author = {Tapas Nayak and Hwee Tou Ng},
journal= {arXiv preprint arXiv:2108.09505},
year = {2021}
}