Abstract
When comparing entities extracted by a medical entity recognition system with gold standard annotations over a test set, two types of mismatches might occur, label mismatch or span mismatch. Here we focus on span mismatch and show that its severity can vary from a serious error to a fully acceptable entity extraction due to the subjectivity of span annotations. For a domain-specific BERT-based NER system, we showed that 25% of the errors have the same labels and overlapping span with gold standard entities. We collected expert judgement which shows more than 90% of these mismatches are accepted or partially accepted by the user. Using the training set of the NER system, we built a fast and lightweight entity classifier to approximate the user experience of such mismatches through accepting or rejecting them. The decisions made by this classifier are used to calculate a learning-based F-score which is shown to be a better approximation of a forgiving user’s experience than the relaxed F-score. We demonstrated the results of applying the proposed evaluation metric for a variety of deep learning medical entity recognition models trained with two datasets.- Anthology ID:
- 2020.bionlp-1.19
- Volume:
- Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 177–186
- Language:
- URL:
- https://aclanthology.org/2020.bionlp-1.19
- DOI:
- 10.18653/v1/2020.bionlp-1.19
- Cite (ACL):
- Isar Nejadgholi, Kathleen C. Fraser, and Berry de Bruijn. 2020. Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience. In Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing, pages 177–186, Online. Association for Computational Linguistics.
- Cite (Informal):
- Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience (Nejadgholi et al., BioNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.bionlp-1.19.pdf
- Code
- nrc-cnrc/NRC-MedNER-Eval
- Data
- MedMentions