ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition
Table 4: Performance on the 601-category fine-grained semantic classification task on the development and test folds using the BERT-NER model.
['[BOLD] Model', '[BOLD] Fold', '[BOLD] Prec.', '[BOLD] Recall', '[BOLD] F1']
We evaluate entity classification performance using the standard definitions of Precision, Recall, and F1. Overall classification performance is high, reaching 0.85 F1 on the held-out test set. This suggests the common entity recognition performance is sufficiently high to be useful for a variety of downstream tasks. To further characterize performance, we investigate how the availability of training data affects this fine-grained classification task, as well as common classes of prediction errors the BERT-NER model makes.