A combination of active learning and self-learning for named entity recognition on Twitter using conditional random fields

Abstract In recent years, many applications in natural language processing (NLP) have been developed using the machine learning approach. Annotating data is an important task in applying machine learning to NLP applications. A common approach to improve the system performance is to train on a large and high-quality set of training data that is annotated by experts. Besides, active learning (AL) and self-learning can be utilized to reduce the annotation costs. The self-learning method discovers highly reliable instances based on a trained classifier, while AL queries the most informative instances based on active query algorithms. This paper proposes a method that combines AL and self-learning to reduce the labeling effort for the named entity recognition task from tweet streams by using both machine-labeled and manually-labeled data. We employ AL queries based on the diversity of the context and content of instances to select the most informative instances. The conditional random fields are also chosen as an underlying model to train a classifier for selecting highly reliable instances. The experiments using Twitter data show that the proposed method achieves good results in reducing the human labeling effort, and it can significantly improve the performance of the systems.