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Semi-supervised learning has been an important approach to address challengesin extracting entities and relations from limited data. However, currentsemi-supervised works handle the two tasks (i.e., Named Entity Recognition andRelation Extraction) separately and ignore the cross-correlation of entity andrelation instances as well as the existence of similar instances acrossunlabeled data. To alleviate the issues, we propose Jointprop, a HeterogeneousGraph-based Propagation framework for joint semi-supervised entity and relationextraction, which captures the global structure information between individualtasks and exploits interactions within unlabeled data. Specifically, weconstruct a unified span-based heterogeneous graph from entity and relationcandidates and propagate class labels based on confidence scores. We thenemploy a propagation learning scheme to leverage the affinities betweenlabelled and unlabeled samples. Experiments on benchmark datasets show that ourframework outperforms the state-of-the-art semi-supervised approaches on NERand RE tasks. We show that the joint semi-supervised learning of the two tasksbenefits from their codependency and validates the importance of utilizing theshared information between unlabeled data.

Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation