Deep Active Learning for Named Entity Recognition
Yanyao Shen, Hyokun Yun, Zachary Lipton, Yakov Kronrod, Animashree Anandkumar
Abstract
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 otherwise: by combining deep learning with active learning, we can outperform classical methods even with a significantly smaller amount of training data.- Anthology ID:
- W17-2630
- Volume:
- Proceedings of the 2nd Workshop on Representation Learning for NLP
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Phil Blunsom, Antoine Bordes, Kyunghyun Cho, Shay Cohen, Chris Dyer, Edward Grefenstette, Karl Moritz Hermann, Laura Rimell, Jason Weston, Scott Yih
- Venue:
- RepL4NLP
- SIG:
- SIGREP
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 252–256
- Language:
- URL:
- https://aclanthology.org/W17-2630
- DOI:
- 10.18653/v1/W17-2630
- Cite (ACL):
- Yanyao Shen, Hyokun Yun, Zachary Lipton, Yakov Kronrod, and Animashree Anandkumar. 2017. Deep Active Learning for Named Entity Recognition. In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 252–256, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Deep Active Learning for Named Entity Recognition (Shen et al., RepL4NLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W17-2630.pdf
- Code
- additional community code