Abstract
Named entity recognition (NER) in clinical documentation is often hindered by the use of highly specialised terminology, variation in language used to express medical findings and general scarcity of high-quality data available for training. This short paper compares a Conditional Random Fields model to the previously established dictionary-based approach and evaluates its ability to extract information from a small corpus of annotated pathology reports. The results suggest that including token descriptors as well as contextual features significantly improves precision on several concept categories while maintaining the same level of recall.- Anthology ID:
- 2023.alta-1.15
- Volume:
- Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association
- Month:
- November
- Year:
- 2023
- Address:
- Melbourne, Australia
- Editors:
- Smaranda Muresan, Vivian Chen, Kennington Casey, Vandyke David, Dethlefs Nina, Inoue Koji, Ekstedt Erik, Ultes Stefan
- Venue:
- ALTA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 130–135
- Language:
- URL:
- https://aclanthology.org/2023.alta-1.15
- DOI:
- Cite (ACL):
- Yang Meng, Vlada Rozova, and Karin Verspoor. 2023. CRF-based recognition of invasive fungal infection concepts in CHIFIR clinical reports. In Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association, pages 130–135, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- CRF-based recognition of invasive fungal infection concepts in CHIFIR clinical reports (Meng et al., ALTA 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.alta-1.15.pdf