Instead of using expensive manual annotations, researchers have proposed to train named entity recognition (NER) systems using heuristic labeling rules. However, devising labeling rules is challenging because it often requires a considerable amount of manual effort and domain expertise. To alleviate this problem, we propose \textsc{GLaRA}, a graph-based labeling rule augmentation framework, to learn new labeling rules from unlabeled data. We first create a graph with nodes representing candidate rules extracted from unlabeled data. Then, we design a new graph neural network to augment labeling rules by exploring the semantic relations between rules. We finally apply the augmented rules on unlabeled data to generate weak labels and train a NER model using the weakly labeled data. We evaluate our method on three NER datasets and find that we can achieve an average improvement of +20\% F1 score over the best baseline when given a small set of seed rules.

研究利用图神经网络GLaRA从无标注数据中学习新标注规则来辅助有命名实体识别（NER）任务的研究。在三个NER数据集上的实验表明，GLaRA可以在用少量种子规则的情况下，在性能指标F1得分上实现平均提升20％。

GLaRA: 基于图形的标注规则增强方法用于弱监督命名实体识别