Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience
| Autor: | Isar Nejadgholi, Berry de Bruijn, Kathleen C. Fraser |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Training set Computer Science - Computation and Language business.industry Computer science Deep learning computer.software_genre Machine Learning (cs.LG) User experience design Error analysis Test set Recognition system Learning based Artificial intelligence business computer Classifier (UML) Computation and Language (cs.CL) Natural language processing |
| Zdroj: | BioNLP |
| DOI: | 10.48550/arxiv.2006.05281 |
| Popis: | 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. 19th SIGBioMed Workshop on Biomedical Language Processing, July 9, 2020, Online |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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