Sid Kiblawi
2023
Interactive Span Recommendation for Biomedical Text
Louis Blankemeier
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Theodore Zhao
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Robert Tinn
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Sid Kiblawi
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Yu Gu
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Akshay Chaudhari
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Hoifung Poon
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Sheng Zhang
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Mu Wei
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J. Preston
Proceedings of the 5th Clinical Natural Language Processing Workshop
Motivated by the scarcity of high-quality labeled biomedical text, as well as the success of data programming, we introduce KRISS-Search. By leveraging the Unified Medical Language Systems (UMLS) ontology, KRISS-Search addresses an interactive few-shot span recommendation task that we propose. We first introduce unsupervised KRISS-Search and show that our method outperforms existing methods in identifying spans that are semantically similar to a given span of interest, with >50% AUPRC improvement relative to PubMedBERT. We then introduce supervised KRISS-Search, which leverages human interaction to improve the notion of similarity used by unsupervised KRISS-Search. Through simulated human feedback, we demonstrate an enhanced F1 score of 0.68 in classifying spans as semantically similar or different in the low-label setting, outperforming PubMedBERT by 2 F1 points. Finally, supervised KRISS-Search demonstrates competitive or superior performance compared to PubMedBERT in few-shot biomedical named entity recognition (NER) across five benchmark datasets, with an average improvement of 5.6 F1 points. We envision KRISS-Search increasing the efficiency of programmatic data labeling and also providing broader utility as an interactive biomedical search engine.
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Co-authors
- Louis Blankemeier 1
- Theodore Zhao 1
- Robert Tinn 1
- Yu Gu 1
- Akshay Chaudhari 1
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