NERVE at ROCLING 2022 Shared Task: A Comparison of Three Named Entity Recognition Frameworks Based on Language Model and Lexicon Approach

Bo-Shau Lin, Jian-He Chen, Tao-Hsing Chang

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.
Anthology ID:
2022.rocling-1.43
Volume:
Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)
Month:
November
Year:
2022
Address:
Taipei, Taiwan
Editors:
Yung-Chun Chang, Yi-Chin Huang
Venue:
ROCLING
SIG:
Publisher:
The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Note:
Pages:
343–349
Language:
Chinese
URL:
https://aclanthology.org/2022.rocling-1.43
DOI:
Bibkey:
Cite (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).
Cite (Informal):
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)
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