Character Feature Learning for Named Entity Recognition

Ping ZENG, Qingping TAN, Haoyu ZHANG, Xiankai MENG, Zhuo ZHANG, Jianjun XU, Yan LEI
2018 IEICE transactions on information and systems  
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
more » ... 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.
doi:10.1587/transinf.2017kbl0001 fatcat:ncteqae5ircmlijx2g4fbmwu6y