%0 Conference Proceedings %T GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition %A Zhao, Xinyan %A Ding, Haibo %A Feng, Zhe %Y Merlo, Paola %Y Tiedemann, Jorg %Y Tsarfaty, Reut %S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume %D 2021 %8 April %I Association for Computational Linguistics %C Online %F zhao-etal-2021-glara %X 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. %R 10.18653/v1/2021.eacl-main.318 %U https://aclanthology.org/2021.eacl-main.318 %U https://doi.org/10.18653/v1/2021.eacl-main.318 %P 3636-3649