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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/W17-2630.pdf
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