LongBEL improves biomedical entity linking consistency by combining full-document context with memory of previous predictions trained via cross-validation rather than gold labels.
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Hybrid neural-symbolic pipeline extracts (action, date) pairs from clinical notes at 0.99 Pair F1 by using BioBERT tagging plus deterministic time normalization, outperforming LLMs on a synthetic benchmark with OOV actions.
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LongBEL: Long-Context and Document-Consistent Biomedical Entity Linking
LongBEL improves biomedical entity linking consistency by combining full-document context with memory of previous predictions trained via cross-validation rather than gold labels.
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Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline
Hybrid neural-symbolic pipeline extracts (action, date) pairs from clinical notes at 0.99 Pair F1 by using BioBERT tagging plus deterministic time normalization, outperforming LLMs on a synthetic benchmark with OOV actions.