2025
Improving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media
Li Y, Viswaroopan D, He W, Li J, Zuo X, Xu H, Tao C. Improving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media. Journal Of Biomedical Informatics 2025, 163: 104789. PMID: 39923968, DOI: 10.1016/j.jbi.2025.104789.Peer-Reviewed Original ResearchConceptsTraditional deep learning modelsDeep learning modelsRecurrent neural networkLearning modelsEntity recognitionLanguage modelF1 scoreEnsemble of deep learningAdvances of natural language processingEffectiveness of ensemble methodsMicro-averaged F1Bidirectional Encoder RepresentationsExtensive labeled dataNatural language processingFine-tuned modelsBiomedical text miningFeature representationEncoder RepresentationsEvent extractionEntity typesText dataDeep learningSequential dataGPT-2Neural network
2024
Augmenting biomedical named entity recognition with general-domain resources
Yin Y, Kim H, Xiao X, Wei C, Kang J, Lu Z, Xu H, Fang M, Chen Q. Augmenting biomedical named entity recognition with general-domain resources. Journal Of Biomedical Informatics 2024, 159: 104731. PMID: 39368529, DOI: 10.1016/j.jbi.2024.104731.Peer-Reviewed Original ResearchBioNER datasetsMulti-task learningNER datasetsEntity typesBiomedical datasetsBaseline modelGeneral domain datasetsBiomedical language modelNeural network-basedYield performance improvementsBioNER modelsEntity recognitionBiomedical corporaHuman annotatorsLabel ambiguityLanguage modelTransfer learningF1 scoreBioNERHuman effortNetwork-basedBiomedical resourcesPerformance improvementDatasetSuperior performance
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