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Distantly supervised datasets for relation extraction mostly focus onsentence-level extraction, and they cover very few relations. In this work, wepropose cross-document relation extraction, where the two entities of arelation tuple appear in two different documents that are connected via a chainof common entities. Following this idea, we create a dataset for two-hoprelation extraction, where each chain contains exactly two documents. Ourproposed dataset covers a higher number of relations than the publiclyavailable sentence-level datasets. We also propose a hierarchical entity graphconvolutional network (HEGCN) model for this task that improves performance by1.1\% F1 score on our two-hop relation extraction dataset, compared to somestrong neural baselines.

A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents