Deep Active Learning for Swedish Named Entity Recognition An empiric evaluation of active learning algorithms for Named Entity Recognition

Abstract

Named entity recognition holds promise for numerous practical applications involving text data, such as keyword extraction and automated anonymization. However, successfully train a machine learning model for Named Entity Recognition is challenging due to the amount of annotated data required, especially for cases where language that is not globally common such as Swedish is involved. In such cases, using a Deep pre-trained model such as BERT in conjunction with the practice of active learning may be preferred. To obtain some insight into the implementation of such an approach, this thesis serves as an empirical study of various active learning strategies when used in conjunction with BERT-based name entity recognition. The performance of different active learning algorithms and the effect of acquisition size on the performance of active learning is the main focus of this study. In conclusion, after comparing and evaluating 17 different active learning methods, the study’s empirical results demonstrate entropy sampling to be the best performing active learning algorithm for Named Entity Recognition of Swedish texts, and the choice of acquisition sizes is practically negligible to performance.