Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction

Wanli Li, Tieyun Qian, Xu Chen, Kejian Tang, Shaohui Zhan, Tao Zhan

Abstract: Manual annotation of labeled data for relation extraction is time-consuming and labor-intensive. Semi-supervised methods can offer helping hands for this problem and have aroused great research interests. Existing works focus on mapping the unlabeled samples to the classes to augment the labeled dataset. However, it is hard to find an overall good mapping function, especially for the samples with complicated syntactic components in one sentence. To tackle this limitation, we propose to build the connection between the unlabeled data and the labeled ones rather than directly mapping the unlabeled samples to the classes. Specifically, we first use two kinds of information to construct a reference graph, including entity reference and verb reference. The goal is to lexically connect the unlabeled sample(s) to the labeled one(s). Then, we develop a Multi-head Reference Graph (MRefG) model to exploit the reference information for better recognizing high-quality unlabeled samples. The effectiveness of our method is demonstrated by extensive comparison experiments with the state-of-the-art baselines. To tackle this limitation, we propose to build the connection between the unlabeled data and the labeled ones rather than directly mapping the unlabeled samples to the classes. Specifically, we first use two kinds of information to construct a reference graph, including entity reference and verb reference. The goal is to lexically connect the unlabeled sample(s) to the labeled one(s). Then, we develop a Multi-head Reference Graph (MRefG) model to exploit the reference information for better recognizing high-quality unlabeled samples. The effectiveness of our method is demonstrated by extensive comparison experiments with the state-of-the-art baselines.