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When comparing entities extracted by a medical entity recognition system withgold 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 thatits severity can vary from a serious error to a fully acceptable entityextraction due to the subjectivity of span annotations. For a domain-specificBERT-based NER system, we showed that 25% of the errors have the same labelsand overlapping span with gold standard entities. We collected expert judgementwhich shows more than 90% of these mismatches are accepted or partiallyaccepted by the user. Using the training set of the NER system, we built a fastand lightweight entity classifier to approximate the user experience of suchmismatches through accepting or rejecting them. The decisions made by thisclassifier are used to calculate a learning-based F-score which is shown to bea better approximation of a forgiving user's experience than the relaxedF-score. We demonstrated the results of applying the proposed evaluation metricfor a variety of deep learning medical entity recognition models trained withtwo datasets.

Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience