IEICE TRANSACTIONS on Information
Character Feature Learning for Named Entity Recognition
Summary :
The deep neural named entity recognition model automatically learns and extracts the features of entities and solves the problem of the traditional model relying heavily on complex feature engineering and obscure professional knowledge. This issue has become a hot topic in recent years. Existing deep neural models only involve simple character learning and extraction methods, which limit their capability. To further explore the performance of deep neural models, we propose two character feature learning models based on convolution neural network and long short-term memory network. These two models consider the local semantic and position features of word characters. Experiments conducted on the CoNLL-2003 dataset show that the proposed models outperform traditional ones and demonstrate excellent performance.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.7 pp.1811-1815
- Publication Date
- 2018/07/01
- Publicized
- 2018/04/20
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017KBL0001
- Type of Manuscript
- Special Section LETTER (Special Section on Knowledge-Based Software Engineering)
- Category
Authors
Ping ZENG
National University of Defense Technology,National Key Laboratory for Parallel and Distributed Processing
Qingping TAN
National University of Defense Technology,National Key Laboratory for Parallel and Distributed Processing
Haoyu ZHANG
National University of Defense Technology,National Key Laboratory for Parallel and Distributed Processing
Xiankai MENG
National University of Defense Technology,National Key Laboratory for Parallel and Distributed Processing
Zhuo ZHANG
National University of Defense Technology,National Key Laboratory for Parallel and Distributed Processing
Jianjun XU
National University of Defense Technology,National Key Laboratory for Parallel and Distributed Processing
Yan LEI
Chongqing University
Keyword
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Ping ZENG, Qingping TAN, Haoyu ZHANG, Xiankai MENG, Zhuo ZHANG, Jianjun XU, Yan LEI, "Character Feature Learning for Named Entity Recognition" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 7, pp. 1811-1815, July 2018, doi: 10.1587/transinf.2017KBL0001.
Abstract: The deep neural named entity recognition model automatically learns and extracts the features of entities and solves the problem of the traditional model relying heavily on complex feature engineering and obscure professional knowledge. This issue has become a hot topic in recent years. Existing deep neural models only involve simple character learning and extraction methods, which limit their capability. To further explore the performance of deep neural models, we propose two character feature learning models based on convolution neural network and long short-term memory network. These two models consider the local semantic and position features of word characters. Experiments conducted on the CoNLL-2003 dataset show that the proposed models outperform traditional ones and demonstrate excellent performance.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017KBL0001/_p
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@ARTICLE{e101-d_7_1811,
author={Ping ZENG, Qingping TAN, Haoyu ZHANG, Xiankai MENG, Zhuo ZHANG, Jianjun XU, Yan LEI, },
journal={IEICE TRANSACTIONS on Information},
title={Character Feature Learning for Named Entity Recognition},
year={2018},
volume={E101-D},
number={7},
pages={1811-1815},
abstract={The deep neural named entity recognition model automatically learns and extracts the features of entities and solves the problem of the traditional model relying heavily on complex feature engineering and obscure professional knowledge. This issue has become a hot topic in recent years. Existing deep neural models only involve simple character learning and extraction methods, which limit their capability. To further explore the performance of deep neural models, we propose two character feature learning models based on convolution neural network and long short-term memory network. These two models consider the local semantic and position features of word characters. Experiments conducted on the CoNLL-2003 dataset show that the proposed models outperform traditional ones and demonstrate excellent performance.},
keywords={},
doi={10.1587/transinf.2017KBL0001},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Character Feature Learning for Named Entity Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 1811
EP - 1815
AU - Ping ZENG
AU - Qingping TAN
AU - Haoyu ZHANG
AU - Xiankai MENG
AU - Zhuo ZHANG
AU - Jianjun XU
AU - Yan LEI
PY - 2018
DO - 10.1587/transinf.2017KBL0001
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2018
AB - The deep neural named entity recognition model automatically learns and extracts the features of entities and solves the problem of the traditional model relying heavily on complex feature engineering and obscure professional knowledge. This issue has become a hot topic in recent years. Existing deep neural models only involve simple character learning and extraction methods, which limit their capability. To further explore the performance of deep neural models, we propose two character feature learning models based on convolution neural network and long short-term memory network. These two models consider the local semantic and position features of word characters. Experiments conducted on the CoNLL-2003 dataset show that the proposed models outperform traditional ones and demonstrate excellent performance.
ER -
English
