Abstract
This paper describes the submission of the TALP-UPC team to the Problem List Summarization task from the BioNLP 2023 workshop. This task consists of automatically extracting a list of health issues from the e-health medical record of a given patient. Our submission combines additional steps of data annotationwith finetuning of BERT pre-trained language models. Our experiments focus on the impact of finetuning on different datasets as well as the addition of data augmentation techniques to delay overfitting.- Anthology ID:
- 2023.bionlp-1.48
- Volume:
- The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Dina Demner-fushman, Sophia Ananiadou, Kevin Cohen
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 497–502
- Language:
- URL:
- https://aclanthology.org/2023.bionlp-1.48
- DOI:
- 10.18653/v1/2023.bionlp-1.48
- Cite (ACL):
- Neil Torrero, Gerard Sant, and Carlos Escolano. 2023. TALP-UPC at ProbSum 2023: Fine-tuning and Data Augmentation Strategies for NER. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 497–502, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- TALP-UPC at ProbSum 2023: Fine-tuning and Data Augmentation Strategies for NER (Torrero et al., BioNLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.bionlp-1.48.pdf