Character Feature Learning for Named Entity Recognition

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

IEICE TRANSACTIONS on Information and Systems   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)
named entity recognition,  character representation learning,  character feature learning,  knowledge extraction,  

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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.