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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/D17-1280.pdf
- Data
- NCBI Datasets, NCBI Disease