Joint Extraction of Entities and Relations via Entity and Relation Heterogeneous Graph Attention Networks

Abstract

Entity and relation extraction (ERE) is a core task in information extraction. This task has always faced the overlap problem. It was found that heterogeneous graph attention networks could enhance semantic analysis and fusion between entities and relations to improve the ERE performance in our previous work. In this paper, an entity and relation heterogeneous graph attention network (ERHGA) is proposed for joint ERE. A heterogeneous graph attention network with a gate mechanism was constructed containing word nodes, subject nodes, and relation nodes to learn and enhance the embedding of parts for relational triple extraction. The ERHGA was evaluated on the public relation extraction dataset named WebNLG. The experimental results demonstrate that the ERHGA, by taking subjects and relations as a priori information, can effectively handle the relational triple extraction problem and outperform all baselines to 93.3%, especially overlapping relational triples.