A Span-Based Distantly Supervised NER with Self-learning

The lack of labeled data is one of the major obstacles for named entity recognition (NER). Distant supervision is often used to alleviate this problem, which automatically generates annotated training datasets by dictionaries. However, as far as we know, existing distant supervision based methods do not consider the latent entities which are not in dictionaries. Intuitively, entities of the same type have the similar contextualized feature, we can use the feature to extract the latent entities within corpuses into corresponding dictionaries to improve the performance of distant supervision based methods. Thus, in this paper, we propose a novel span-based self-learning method, which employs span-level features to update corresponding dictionaries. Specifically, the proposed method directly takes all possible spans into account and scores them for each label, then picks latent entities from candidate spans into corresponding dictionaries based on both local and global features. Extensive experiments on two public datasets show that our proposed method performs better than the state-of-the-art baselines.