MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation

Thomas Searle, Zeljko Kraljevic, Rebecca Bendayan, Daniel Bean, Richard Dobson


Abstract
An interface for building, improving and customising a given Named Entity Recognition and Linking (NER+L) model for biomedical domain text, and the efficient collation of accurate research use case specific training data and subsequent model training. Screencast demo available here: https://www.youtube.com/watch?v=lM914DQjvSo
Anthology ID:
D19-3024
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Sebastian Padó, Ruihong Huang
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
139–144
Language:
URL:
https://aclanthology.org/D19-3024
DOI:
10.18653/v1/D19-3024
Bibkey:
Cite (ACL):
Thomas Searle, Zeljko Kraljevic, Rebecca Bendayan, Daniel Bean, and Richard Dobson. 2019. MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pages 139–144, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation (Searle et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-3024.pdf