  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.
