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        <title>SCU-NLP at ROCLING 2022 Shared Task: Experiment and Error Analysis of Biomedical Entity Detection Model</title>
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    <abstract>Named entity recognition generally refers to entities with specific meanings in unstructured text, including names of people, places, organizations, dates, times, quantities, proper nouns and other words. In the medical field, it may be drug names, Organ names, test items, nutritional supplements, etc. The purpose of named entity recognition in this study is to search for the above items from unstructured input text. In this study, taking healthcare as the research purpose, and predicting named entity boundaries and categories of sentences based on ten entity types, We explore multiple fundamental NER approaches to solve this task, Include: Hidden Markov Models, Conditional Random Fields, Random Forest Classifier and BERT. The prediction results are more significant in the F-score of the CRF model, and have achieved better results.</abstract>
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%0 Conference Proceedings
%T SCU-NLP at ROCLING 2022 Shared Task: Experiment and Error Analysis of Biomedical Entity Detection Model
%A Chiou, Sung-Ting
%A Huang, Sheng-Wei
%A Lo, Ying-Chun
%A Wu, Yu-Hsuan
%A Wu, Jheng-Long
%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 chiou-etal-2022-scu
%X Named entity recognition generally refers to entities with specific meanings in unstructured text, including names of people, places, organizations, dates, times, quantities, proper nouns and other words. In the medical field, it may be drug names, Organ names, test items, nutritional supplements, etc. The purpose of named entity recognition in this study is to search for the above items from unstructured input text. In this study, taking healthcare as the research purpose, and predicting named entity boundaries and categories of sentences based on ten entity types, We explore multiple fundamental NER approaches to solve this task, Include: Hidden Markov Models, Conditional Random Fields, Random Forest Classifier and BERT. The prediction results are more significant in the F-score of the CRF model, and have achieved better results.
%U https://aclanthology.org/2022.rocling-1.44
%P 350-355

Markdown (Informal)

[SCU-NLP at ROCLING 2022 Shared Task: Experiment and Error Analysis of Biomedical Entity Detection Model](https://aclanthology.org/2022.rocling-1.44) (Chiou et al., ROCLING 2022)

• SCU-NLP at ROCLING 2022 Shared Task: Experiment and Error Analysis of Biomedical Entity Detection Model (Chiou et al., ROCLING 2022)

ACL

• Sung-Ting Chiou, Sheng-Wei Huang, Ying-Chun Lo, Yu-Hsuan Wu, and Jheng-Long Wu. 2022. SCU-NLP at ROCLING 2022 Shared Task: Experiment and Error Analysis of Biomedical Entity Detection Model. In Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022), pages 350–355, Taipei, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).