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Biomedical named entity normalization via interaction-based synonym marginalization.

Hao Peng1, Ying Xiong1, Yang Xiang2

  • 1Department of Computer Science, Harbin Institute of Technology, Shenzhen 518055, China.

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|November 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces IA-BIOSYN, a new method for biomedical named entity normalization (BNEN) that considers relationships among candidate terms. IA-BIOSYN significantly improves BNEN accuracy by effectively capturing mention-candidate and candidate-candidate relations.

Keywords:
Biomedical named entity normalizationBiomedical named entity-candidate interactionCandidate-candidate interactionSynonym marginalization

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Area of Science:

  • Biomedical Natural Language Processing
  • Computational Biology
  • Bioinformatics

Background:

  • Biomedical Named Entity Normalization (BNEN) is crucial for processing biomedical text.
  • Existing methods often encode entities and candidates separately, overlooking inter-candidate relationships.
  • Capturing relations among candidates can enhance BNEN performance.

Purpose of the Study:

  • To propose a novel interaction-based synonym marginalization method for BNEN.
  • To effectively model both mention-candidate and candidate-candidate relations.
  • To improve the accuracy of biomedical named entity normalization.

Main Methods:

  • A novel method, IA-BIOSYN, is introduced for BNEN.
  • It dynamically selects candidates for a given biomedical named entity mention (BNEM).
  • An interaction module models BNEM-candidate and candidate-candidate relations, followed by synonym marginalization.

Main Results:

  • IA-BIOSYN achieved high accuracy on three public datasets: NCBI-Disease (0.9333), BC5CDR-Disease (0.9379), and BC5CDR-Chemical (0.9693).
  • The proposed method significantly outperformed other state-of-the-art (SOTA) methods.
  • Results demonstrate the effectiveness of capturing inter-candidate relations.

Conclusions:

  • Both mention-candidate and candidate-candidate relations are vital for effective BNEN.
  • The proposed IA-BIOSYN method successfully captures these crucial relationships.
  • IA-BIOSYN offers a significant advancement in biomedical named entity normalization accuracy.