GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition
Xinyan Zhao, Haibo Ding, Zhe Feng · 2021
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 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.