NITE: A Neural Inductive Teaching Framework for Domain Specific NER

Siliang Tang, Ning Zhang, Jinjiang Zhang, Fei Wu, Yueting Zhuang


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.
Anthology ID:
D17-1280
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Martha Palmer, Rebecca Hwa, Sebastian Riedel
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2652–2657
Language:
URL:
https://aclanthology.org/D17-1280
DOI:
10.18653/v1/D17-1280
Bibkey:
Cite (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.
Cite (Informal):
NITE: A Neural Inductive Teaching Framework for Domain Specific NER (Tang et al., EMNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/D17-1280.pdf
Data
NCBI DatasetsNCBI Disease