Preserving Empirical Probabilities in BERT for Small-sample Clinical Entity Recognition
This work addresses label imbalance in clinical NER, which is crucial for accurate and equitable entity recognition in healthcare, but appears incremental as it builds on existing BERT methods.
The paper tackled the problem of unbalanced labels in clinical Named Entity Recognition, which leads to biased models and poor performance on minority entity classes, by analyzing BERT-based models and proposing improvements for token classification on imbalanced datasets.
Named Entity Recognition (NER) encounters the challenge of unbalanced labels, where certain entity types are overrepresented while others are underrepresented in real-world datasets. This imbalance can lead to biased models that perform poorly on minority entity classes, impeding accurate and equitable entity recognition. This paper explores the effects of unbalanced entity labels of the BERT-based pre-trained model. We analyze the different mechanisms of loss calculation and loss propagation for the task of token classification on randomized datasets. Then we propose ways to improve the token classification for the highly imbalanced task of clinical entity recognition.