@inproceedings{lee-etal-2022-ncuee,
title = "{NCUEE}-{NLP} at {S}em{E}val-2022 Task 11: {C}hinese Named Entity Recognition Using the {BERT}-{B}i{LSTM}-{CRF} Model",
author = "Lee, Lung-Hao and
Lu, Chien-Huan and
Lin, Tzu-Mi",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.220",
doi = "10.18653/v1/2022.semeval-1.220",
pages = "1597--1602",
abstract = "This study describes the model design of the NCUEE-NLP system for the Chinese track of the SemEval-2022 MultiCoNER task. We use the BERT embedding for character representation and train the BiLSTM-CRF model to recognize complex named entities. A total of 21 teams participated in this track, with each team allowed a maximum of six submissions. Our best submission, with a macro-averaging F1-score of 0.7418, ranked the seventh position out of 21 teams.",
}
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%0 Conference Proceedings
%T NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model
%A Lee, Lung-Hao
%A Lu, Chien-Huan
%A Lin, Tzu-Mi
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F lee-etal-2022-ncuee
%X This study describes the model design of the NCUEE-NLP system for the Chinese track of the SemEval-2022 MultiCoNER task. We use the BERT embedding for character representation and train the BiLSTM-CRF model to recognize complex named entities. A total of 21 teams participated in this track, with each team allowed a maximum of six submissions. Our best submission, with a macro-averaging F1-score of 0.7418, ranked the seventh position out of 21 teams.
%R 10.18653/v1/2022.semeval-1.220
%U https://aclanthology.org/2022.semeval-1.220
%U https://doi.org/10.18653/v1/2022.semeval-1.220
%P 1597-1602
Markdown (Informal)
[NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model](https://aclanthology.org/2022.semeval-1.220) (Lee et al., SemEval 2022)
ACL