GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition

• URL: http://arxiv.org/abs/2104.06230v1
• Date: Tue, 13 Apr 2021 14:20:58 GMT
• Title: GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition
• Authors: Xinyan Zhao, Haibo Ding, Zhe Feng
• Abstract summary: We propose textscGLaRA, a graph-based labeling rule augmentation framework, to learn new labeling rules from unlabeled data. We apply the augmented rules on unlabeled data to generate weak labels and train a NER model using the weakly labeled data.
• Score: 8.352789684571704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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