GraphRel: Modeling Text as Relational Graphs for Joint Entity andRelation Extraction
Abstract
In this paper, we present GraphRel, an
end-to-end relation extraction model which
uses graph convolutional networks (GCNs) to
jointly learn named entities and relations. In
contrast to previous baselines, we consider the
interaction between named entities and relations via a relation-weighted GCN to better extract relations. Linear and dependency structures are both used to extract both sequential
and regional features of the text, and a complete word graph is further utilized to extract
implicit features among all word pairs of the
text. With the graph-based approach, the prediction for overlapping relations is substantially improved over previous sequential approaches. We evaluate GraphRel on two public datasets: NYT and WebNLG. Results show
that GraphRel maintains high precision while
increasing recall substantially. Also, GraphRel
outperforms previous work by 3.2% and 5.8%
(F1 score), achieving a new state-of-the-art for
relation extraction.