Deep Active Learning for Named Entity Recognition
Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, Animashree Anandkumar · 2017
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 that by combining deep learning with active learning, we can outperform classical methods even with a significantly smaller amount of training data.