%0 Conference Proceedings %T NITE: A Neural Inductive Teaching Framework for Domain Specific NER %A Tang, Siliang %A Zhang, Ning %A Zhang, Jinjiang %A Wu, Fei %A Zhuang, Yueting %Y Palmer, Martha %Y Hwa, Rebecca %Y Riedel, Sebastian %S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing %D 2017 %8 September %I Association for Computational Linguistics %C Copenhagen, Denmark %F tang-etal-2017-nite %X In domain-specific NER, due to insufficient labeled training data, deep models usually fail to behave normally. In this paper, we proposed a novel Neural Inductive TEaching framework (NITE) to transfer knowledge from existing domain-specific NER models into an arbitrary deep neural network in a teacher-student training manner. NITE is a general framework that builds upon transfer learning and multiple instance learning, which collaboratively not only transfers knowledge to a deep student network but also reduces the noise from teachers. NITE can help deep learning methods to effectively utilize existing resources (i.e., models, labeled and unlabeled data) in a small domain. The experiment resulted on Disease NER proved that without using any labeled data, NITE can significantly boost the performance of a CNN-bidirectional LSTM-CRF NER neural network nearly over 30% in terms of F1-score. %R 10.18653/v1/D17-1280 %U https://aclanthology.org/D17-1280 %U https://doi.org/10.18653/v1/D17-1280 %P 2652-2657