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    <abstract>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.</abstract>
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%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

Markdown (Informal)

[NITE: A Neural Inductive Teaching Framework for Domain Specific NER](https://aclanthology.org/D17-1280) (Tang et al., EMNLP 2017)

• NITE: A Neural Inductive Teaching Framework for Domain Specific NER (Tang et al., EMNLP 2017)

ACL

• Siliang Tang, Ning Zhang, Jinjiang Zhang, Fei Wu, and Yueting Zhuang. 2017. NITE: A Neural Inductive Teaching Framework for Domain Specific NER. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2652–2657, Copenhagen, Denmark. Association for Computational Linguistics.