Abstract
Chinese word segmentation (CWS) and named entity recognition (NER) are two important tasks in Chinese natural language processing. To achieve good model performance on these tasks, existing neural approaches normally require a large amount of labeled training data, which is often unavailable for specific domains such as the Chinese medical domain due to privacy and legal issues. To address this problem, we have developed a Chinese medical corpus named ChiMST which consists of question-answer pairs collected from an online medical healthcare platform and is annotated with word boundary and medical term information. For word boundary, we mainly follow the word segmentation guidelines for the Penn Chinese Treebank (Xia, 2000); for medical terms, we define 9 categories and 18 sub-categories after consulting medical experts. To provide baselines on this corpus, we train existing state-of-the-art models on it and achieve good performance. We believe that the corpus and the baseline systems will be a valuable resource for CWS and NER research on the medical domain.- Anthology ID:
- 2022.lrec-1.607
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 5654–5664
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.607
- DOI:
- Cite (ACL):
- Yuanhe Tian, Han Qin, Fei Xia, and Yan Song. 2022. ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 5654–5664, Marseille, France. European Language Resources Association.
- Cite (Informal):
- ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition (Tian et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.lrec-1.607.pdf
- Code
- synlp/chimst