Deep Active Learning for Named Entity Recognition

Yanyao Shen, Hyokun Yun, Zachary Chase Lipton, Yakov Kronrod, Animashree Anandkumar

Abstract: Deep neural networks have advanced the state of the art in named entity recognition. However, under typical training procedures, advantages over classical methods emerge only with large datasets. As a result, deep learning is employed only when large public datasets or a large budget for manually labeling data is available. In this work, we show otherwise: by combining deep learning with active learning, we can outperform classical methods even with a significantly smaller amount of training data.