  Semi-supervised learning has been an important approach to address challenges
in extracting entities and relations from limited data. However, current
semi-supervised works handle the two tasks (i.e., Named Entity Recognition and
Relation Extraction) separately and ignore the cross-correlation of entity and
relation instances as well as the existence of similar instances across
unlabeled data. To alleviate the issues, we propose Jointprop, a Heterogeneous
Graph-based Propagation framework for joint semi-supervised entity and relation
extraction, which captures the global structure information between individual
tasks and exploits interactions within unlabeled data. Specifically, we
construct a unified span-based heterogeneous graph from entity and relation
candidates and propagate class labels based on confidence scores. We then
employ a propagation learning scheme to leverage the affinities between
labelled and unlabeled samples. Experiments on benchmark datasets show that our
framework outperforms the state-of-the-art semi-supervised approaches on NER
and RE tasks. We show that the joint semi-supervised learning of the two tasks
benefits from their codependency and validates the importance of utilizing the
shared information between unlabeled data.
