Mitigating Effect of Dictionary Matching Errors in Distantly Supervised Named Entity Recognition

Named entity recognition (NER) is a fundamental technique that brings basic semantic awareness to natural language processing applications and services. Since we need a large amount of training data to train a custom NER model, distant supervision that leverages named entity dictionaries is expected to be a promising approach to train NER models quickly. However, dictionary matching causes a considerable number of errors that deteriorates both precision and recall of the final NER models, and we need to mitigate its effect. In this study, we particularly aim at improving precision of NER models by accounting for dictionary matching errors. Experimental results show that the proposed method can achieve an improvement of precisions especially under poor dictionary performance conditions.