Abstract
Name Entity Recognition (NER) is a very important and basic task in traditional NLP tasks. In the biomedical field, NER tasks have been widely used in various products developed by various manufacturers. These include parsing, QA system, key information extraction or replacement in dialogue systems, and the practical application of knowledge parsing. In different fields, including bio-medicine, communication technology, e-commerce etc., NER technology is needed to identify drugs, diseases, commodities and other objects. This implementation focuses on the CLING 2022 SHARED TASK’s(Lee et al. 2022) NER TASK in biomedical field, with a bit of tuning and experimentation based on the language models.- Anthology ID:
- 2022.rocling-1.39
- 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:
- 316–320
- Language:
- Chinese
- URL:
- https://aclanthology.org/2022.rocling-1.39
- DOI:
- Cite (ACL):
- Zhi-Quan Feng, Po-Kai Chen, and Jia-Ching Wang. 2022. NCU1415 at ROCLING 2022 Shared Task: A light-weight transformer-based approach for Biomedical Name Entity Recognition. In Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022), pages 316–320, Taipei, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).
- Cite (Informal):
- NCU1415 at ROCLING 2022 Shared Task: A light-weight transformer-based approach for Biomedical Name Entity Recognition (Feng et al., ROCLING 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.rocling-1.39.pdf