Z. Huang, W. Xu, and K. Yu, Bidirectional lstm-crf models for sequence tagging, 2015.

Z. Jie and W. Lu, Dependency-guided lstmcrf for named entity recognition, 2019.

J. Lafferty, A. Mccallum, and F. Pereira, Conditional random fields : Probabilistic models for segmenting and labeling sequence data, 2001.

G. Lample, M. Ballesteros, S. Subramanian, K. Kawakami, and C. Dyer, Neural architectures for named entity recognition, 2016.

J. Li, A. Sun, J. Han, and C. Li, A survey on deep learning for named entity recognition, 2018.

T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, Distributed representations of words and phrases and their compositionality, Advances in neural information processing systems, pp.3111-3119, 2013.

, Cicero Nogueira dos Santos and Victor Guimaraes. Boosting named entity recognition with neural character embeddings, 2015.

C. Sutton and A. Mccallum, An introduction to conditional random fields. Foundations and Trends R in Machine Learning, vol.4, pp.267-373, 2012.

L. Taylor and G. Nitschke, Improving deep learning using generic data augmentation, 2017.

V. Yadav and S. Bethard, A survey on recent advances in named entity recognition from deep learning models, 2019.

Z. Zhai, K. Dat-quoc-nguyen, and . Verspoor, Comparing cnn and lstm character-level embeddings in bilstm-crf models for chemical and disease named entity recognition, 2018.