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Character Feature Learning for Named Entity Recognition

Ping ZENG, Qingping TAN, Haoyu ZHANG, Xiankai MENG, Zhuo ZHANG, Jianjun XU, Yan LEI

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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

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