Joint Extraction of Entities and Relations via Entity and Relation Heterogeneous Graph Attention Networks
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Topic Modeling · Advanced Graph Neural Networks · Data Quality and Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China
Funding
National Natural Science Foundation of China
References (37)
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