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

• URL: http://arxiv.org/abs/2305.15872v1
• Date: Thu, 25 May 2023 09:07:04 GMT
• Title: Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation
• Authors: Yandan Zheng, Anran Hao, Anh Tuan Luu
• Abstract summary: We propose Jointprop, a Heterogeneous Graph-based Propagation framework for joint semi-supervised entity and relation extraction. We construct a unified span-based heterogeneous graph from entity and relation candidates and propagate class labels based on confidence scores. We show that our framework outperforms the state-of-the-art semi-supervised approaches on NER and RE tasks.
• Score: 13.418617500641401
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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