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.
Recently, REF matched state of the art performance on named entity recognition, using just 25% of the training data.
Deep Active Learning for Named Entity Recognition
ArXiv
ArXiv
Computer Science