@inproceedings{lei-etal-2020-neural,
title = "Neural Language Modeling for Named Entity Recognition",
author = "Lei, Zhihong and
Wang, Weiyue and
Dugast, Christian and
Ney, Hermann",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.612",
doi = "10.18653/v1/2020.coling-main.612",
pages = "6937--6941",
abstract = "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.",
}
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%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
Markdown (Informal)
[Neural Language Modeling for Named Entity Recognition](https://aclanthology.org/2020.coling-main.612) (Lei et al., COLING 2020)
ACL
- Zhihong Lei, Weiyue Wang, Christian Dugast, and Hermann Ney. 2020. Neural Language Modeling for Named Entity Recognition. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6937–6941, Barcelona, Spain (Online). International Committee on Computational Linguistics.