@inproceedings{shen-etal-2017-deep,
title = "Deep Active Learning for Named Entity Recognition",
author = "Shen, Yanyao and
Yun, Hyokun and
Lipton, Zachary and
Kronrod, Yakov and
Anandkumar, Animashree",
editor = "Blunsom, Phil and
Bordes, Antoine and
Cho, Kyunghyun and
Cohen, Shay and
Dyer, Chris and
Grefenstette, Edward and
Hermann, Karl Moritz and
Rimell, Laura and
Weston, Jason and
Yih, Scott",
booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2630",
doi = "10.18653/v1/W17-2630",
pages = "252--256",
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.",
}
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%0 Conference Proceedings
%T Deep Active Learning for Named Entity Recognition
%A Shen, Yanyao
%A Yun, Hyokun
%A Lipton, Zachary
%A Kronrod, Yakov
%A Anandkumar, Animashree
%Y Blunsom, Phil
%Y Bordes, Antoine
%Y Cho, Kyunghyun
%Y Cohen, Shay
%Y Dyer, Chris
%Y Grefenstette, Edward
%Y Hermann, Karl Moritz
%Y Rimell, Laura
%Y Weston, Jason
%Y Yih, Scott
%S Proceedings of the 2nd Workshop on Representation Learning for NLP
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F shen-etal-2017-deep
%X 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.
%R 10.18653/v1/W17-2630
%U https://aclanthology.org/W17-2630
%U https://doi.org/10.18653/v1/W17-2630
%P 252-256
Markdown (Informal)
[Deep Active Learning for Named Entity Recognition](https://aclanthology.org/W17-2630) (Shen et al., RepL4NLP 2017)
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.