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Improving biomedical entity linking with generative relevance feedback.

Darya Shlyk1, Lawrence Hunter2

  • 1Department of Computer Science, Università degli Studi di Milano, Milan 20133, Italy.

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Summary
This summary is machine-generated.

Generative Relevance Feedback (GRF) enhances biomedical entity linking (BEL) by improving candidate retrieval using large language models (LLMs). This approach boosts accuracy and recall, advancing normalization performance in BEL systems.

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Knowledge Discovery

Background:

  • Biomedical Entity Linking (BEL) is crucial for mapping text mentions to standardized identifiers.
  • Current BEL systems face limitations in recall due to suboptimal candidate retrieval.
  • This restricts the overall effectiveness of biomedical text normalization.

Purpose of the Study:

  • To systematically evaluate Generative Relevance Feedback (GRF) for improving candidate retrieval in BEL.
  • To assess GRF's impact on direct linking prediction and cascading normalization pipelines.
  • To analyze GRF's sensitivity to different LLMs, feedback types, and integration strategies.

Main Methods:

  • Implemented GRF leveraging large language models (LLMs) for zero-shot mention enrichment.
  • Evaluated GRF in direct linking prediction and candidate generation scenarios.
  • Conducted experiments across eight corpora and four biomedical knowledge bases.

Main Results:

  • GRF significantly improved both accuracy and recall in BEL candidate retrieval.
  • Enhanced performance increased the upper bound for normalization.
  • Demonstrated GRF's effectiveness across diverse biomedical datasets and knowledge bases.

Conclusions:

  • GRF offers an efficient and model-agnostic solution for enhancing BEL.
  • GRF has the potential to be a key component in advancing biomedical entity linking.
  • The study provides a systematic evaluation and reproducible code for GRF in BEL.