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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2 Pith papers cite this work, alongside 243 external citations. Polarity classification is still indexing.
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An endpoint point-relation classifier followed by decoding to interval relations achieves 70.1% temporal awareness on TempEval-3, setting a new state-of-the-art for the full set of fine-grained temporal relations.
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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.
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Looking for the Bottleneck in Fine-grained Temporal Relation Classification
An endpoint point-relation classifier followed by decoding to interval relations achieves 70.1% temporal awareness on TempEval-3, setting a new state-of-the-art for the full set of fine-grained temporal relations.