An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization

Traditional pipeline models for medical named entity recognition and normalization (MER and MEN) suffer from error propagation. To tackle the error propagation problem, we propose a novel joint deep learning method for the 2020 IberLEF shared task on MER and MEN, where MER is regarded as a machine reading comprehension (MRC) problem and MEN as multiple sequence labeling problems corresponding to normalized hierarchical tumor codes. In the 2020 IberLEF shared task, our proposed joint model achieves an F1 score of 0.87 on MER and an F1 score of 0.825 on MEN, and significantly outperforms pipeline models for comparison.