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    This study introduces a novel community-based decision-making system to improve Named Entity Recognition (NER) for chemical and drug names. The proposed method enhances NER system performance, outperforming existing strategies.

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    Area of Science:

    • Biomedical Informatics
    • Computational Biology
    • Natural Language Processing

    Background:

    • Named Entity Recognition (NER) is crucial for text mining in the biochemical domain.
    • Improving NER system performance for chemical and drug names remains a significant challenge.

    Purpose of the Study:

    • To develop a community-based decision-making system to enhance NER efficiency for chemical and drug names.
    • To improve the accuracy and performance of NER systems in the biochemical domain.

    Main Methods:

    • A two-step approach was implemented: creating baseline NER classifiers using Conditional Random Fields (CRFs) with diverse feature sets.
    • Particle Swarm Optimization (PSO) was employed for expert classifier selection and Bayesian methods for output combination and fitness evaluation.

    Main Results:

    • The proposed ensemble system demonstrated superior performance compared to single classifiers and other ensemble strategies on the ChemDNER and CEMP corpora.
    • The method achieved an F-score of 87.95%, surpassing the top system in the BioCreative IV ChemDNER track.

    Conclusions:

    • The community-based decision-making system effectively enhances NER performance for chemical and drug names.
    • The integration of PSO and Bayesian combination offers a robust strategy for optimizing NER systems in bioinformatics.