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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 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 languageEnglish
Title of host publicationSemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop
EditorsGuy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
PublisherAssociation for Computational Linguistics (ACL)
Pages1597-1602
Number of pages6
ISBN (Electronic)9781955917803
StatePublished - 2022
Event16th International Workshop on Semantic Evaluation, SemEval 2022 - Seattle, United States
Duration: 14 Jul 202215 Jul 2022

Publication series

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

Conference

Conference16th International Workshop on Semantic Evaluation, SemEval 2022
Country/TerritoryUnited States
CitySeattle
Period14/07/2215/07/22

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