AIBullisharXiv – CS AI · Mar 35/104
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Noise reduction in BERT NER models for clinical entity extraction
Researchers developed a Noise Removal model to improve precision in clinical entity extraction using BERT-based Named Entity Recognition systems. The model uses advanced features like Probability Density Maps to identify weak vs strong predictions, reducing false positives by 50-90% in clinical NER applications.