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        <title>NERVE at ROCLING 2022 Shared Task: A Comparison of Three Named Entity Recognition Frameworks Based on Language Model and Lexicon Approach</title>
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        <namePart type="given">Bo-Shau</namePart>
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    <name type="personal">
        <namePart type="given">Tao-Hsing</namePart>
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            <title>Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)</title>
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        <name type="personal">
            <namePart type="given">Yung-Chun</namePart>
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        <name type="personal">
            <namePart type="given">Yi-Chin</namePart>
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    <abstract>ROCLING 2022 shared task is to design a method that can tag medical entities in sentences and then classify them into categories through an algorithm. This paper proposes three models to deal with NER issues. The first is a BERT model combined with a classifier. The second is a two-stage model, where the first stage is to use a BERT model combined with a classifier for detecting whether medical entities exist in a sentence, and the second stage focuses on classifying the entities into categories. The third approach is to combine the first two models and a model based on the lexicon approach, integrating the outputs of the three models and making predictions. The prediction results of the three models for the validation and testing datasets show little difference in the performance of the three models, with the best performance on the F1 indicator being 0.7569 for the first model.</abstract>
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%0 Conference Proceedings
%T NERVE at ROCLING 2022 Shared Task: A Comparison of Three Named Entity Recognition Frameworks Based on Language Model and Lexicon Approach
%A Lin, Bo-Shau
%A Chen, Jian-He
%A Chang, Tao-Hsing
%Y Chang, Yung-Chun
%Y Huang, Yi-Chin
%S Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)
%D 2022
%8 November
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taipei, Taiwan
%G Chinese
%F lin-etal-2022-nerve
%X ROCLING 2022 shared task is to design a method that can tag medical entities in sentences and then classify them into categories through an algorithm. This paper proposes three models to deal with NER issues. The first is a BERT model combined with a classifier. The second is a two-stage model, where the first stage is to use a BERT model combined with a classifier for detecting whether medical entities exist in a sentence, and the second stage focuses on classifying the entities into categories. The third approach is to combine the first two models and a model based on the lexicon approach, integrating the outputs of the three models and making predictions. The prediction results of the three models for the validation and testing datasets show little difference in the performance of the three models, with the best performance on the F1 indicator being 0.7569 for the first model.
%U https://aclanthology.org/2022.rocling-1.43
%P 343-349

Markdown (Informal)

[NERVE at ROCLING 2022 Shared Task: A Comparison of Three Named Entity Recognition Frameworks Based on Language Model and Lexicon Approach](https://aclanthology.org/2022.rocling-1.43) (Lin et al., ROCLING 2022)

• NERVE at ROCLING 2022 Shared Task: A Comparison of Three Named Entity Recognition Frameworks Based on Language Model and Lexicon Approach (Lin et al., ROCLING 2022)

ACL

• Bo-Shau Lin, Jian-He Chen, and Tao-Hsing Chang. 2022. NERVE at ROCLING 2022 Shared Task: A Comparison of Three Named Entity Recognition Frameworks Based on Language Model and Lexicon Approach. In Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022), pages 343–349, Taipei, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).