Combining Self Learning and Active Learning for Chinese Named Entity Recognition

Abstract

This paper proposes a combination of active learning and self-training method to reduce the labeling effort for Chinese Named Entity Recognition (NER). Active learning and self-training are two different ways to use unlabeled data. They are complement when choosing unlabeled data for further training. A new strategy based on Information Density (ID) for sample selecting in sequential labeling problem is also proposed, which is suitable for both active learning and self-training. Conditional Random Fields (CRFs) is chosen as the underlying model for active learning and self-training in the proposed approach due to its promising performance in many sequence labeling tasks. Experiment results show the effect of the proposed method. On Sighan bakeoff 2006 MSRA NER corpus, an F1 score of 77.4% is achieved by using only 15,000 training sentences chosen by the proposed hybrid method.