%0 Conference Proceedings %T Neural Language Modeling for Named Entity Recognition %A Lei, Zhihong %A Wang, Weiyue %A Dugast, Christian %A Ney, Hermann %Y Scott, Donia %Y Bel, Nuria %Y Zong, Chengqing %S Proceedings of the 28th International Conference on Computational Linguistics %D 2020 %8 December %I International Committee on Computational Linguistics %C Barcelona, Spain (Online) %F lei-etal-2020-neural %X Named entity recognition is a key component in various natural language processing systems, and neural architectures provide significant improvements over conventional approaches. Regardless of different word embedding and hidden layer structures of the networks, a conditional random field layer is commonly used for the output. This work proposes to use a neural language model as an alternative to the conditional random field layer, which is more flexible for the size of the corpus. Experimental results show that the proposed system has a significant advantage in terms of training speed, with a marginal performance degradation. %R 10.18653/v1/2020.coling-main.612 %U https://aclanthology.org/2020.coling-main.612 %U https://doi.org/10.18653/v1/2020.coling-main.612 %P 6937-6941