Bagging-based active learning model for named entity recognition with distant supervision

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Abstract
Named entity recognition (NER) is a preliminary step to performing information extraction and question answering. Most previous studies on NER have been based on supervised machine learning methods that need a large amount of human-annotated training corpus. In this paper, we propose a semi-supervised NER model to minimize the time-consuming and labor-intensive task for constructing the training corpus. The proposed model generates weakly labeled training corpus using a distant supervision method. Then, it improves NER accuracy by refining the weakly labeled training corpus using a bagging-based active learning method. In the experiments, the proposed model outperformed the previous semi-supervised model. It showed F1-measure of 0.764 after 15 times of bagging-based active learning.