NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model

Lung Hao Lee; Chien Huan Lu; Tzu Mi Lin

doi:10.18653/v1/2022.semeval-1.220

NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model

Lung Hao Lee, Chien Huan Lu, Tzu Mi Lin

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

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.

Original language	English
Title of host publication	SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop
Editors	Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Publisher	Association for Computational Linguistics (ACL)
Pages	1597-1602
Number of pages	6
ISBN (Electronic)	9781955917803
DOIs	https://doi.org/10.18653/v1/2022.semeval-1.220
State	Published - 2022
Event	16th International Workshop on Semantic Evaluation, SemEval 2022 - Seattle, United States Duration: 14 Jul 2022 → 15 Jul 2022

Publication series

Name	SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop

Conference

Conference	16th International Workshop on Semantic Evaluation, SemEval 2022
Country/Territory	United States
City	Seattle
Period	14/07/22 → 15/07/22

Access to Document

10.18653/v1/2022.semeval-1.220

Chinese Knowledge Base Construction and Applications for Medical Healthcare Domain(3/3)
Lee, L.-H. (PI)
1/05/21 → 31/07/22
Project: Research

Cite this

Lee, L. H., Lu, C. H., & Lin, T. M. (2022). NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model. In G. Emerson, N. Schluter, G. Stanovsky, R. Kumar, A. Palmer, N. Schneider, S. Singh, & S. Ratan (Eds.), SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop (pp. 1597-1602). (SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.semeval-1.220

Lee, Lung Hao ; Lu, Chien Huan ; Lin, Tzu Mi. / NCUEE-NLP at SemEval-2022 Task 11 : Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model. SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop. editor / Guy Emerson ; Natalie Schluter ; Gabriel Stanovsky ; Ritesh Kumar ; Alexis Palmer ; Nathan Schneider ; Siddharth Singh ; Shyam Ratan. Association for Computational Linguistics (ACL), 2022. pp. 1597-1602 (SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop).

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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.",

author = "Lee, {Lung Hao} and Lu, {Chien Huan} and Lin, {Tzu Mi}",

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Lee, LH, Lu, CH & Lin, TM 2022, NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model. in G Emerson, N Schluter, G Stanovsky, R Kumar, A Palmer, N Schneider, S Singh & S Ratan (eds), SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop. SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop, Association for Computational Linguistics (ACL), pp. 1597-1602, 16th International Workshop on Semantic Evaluation, SemEval 2022, Seattle, United States, 14/07/22. https://doi.org/10.18653/v1/2022.semeval-1.220

NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model. / Lee, Lung Hao; Lu, Chien Huan; Lin, Tzu Mi.
SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop. ed. / Guy Emerson; Natalie Schluter; Gabriel Stanovsky; Ritesh Kumar; Alexis Palmer; Nathan Schneider; Siddharth Singh; Shyam Ratan. Association for Computational Linguistics (ACL), 2022. p. 1597-1602 (SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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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Lee LH, Lu CH, Lin TM. NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model. In Emerson G, Schluter N, Stanovsky G, Kumar R, Palmer A, Schneider N, Singh S, Ratan S, editors, SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop. Association for Computational Linguistics (ACL). 2022. p. 1597-1602. (SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop). doi: 10.18653/v1/2022.semeval-1.220

NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model

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Chinese Knowledge Base Construction and Applications for Medical Healthcare Domain(3/3)

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