2024
Advancing entity recognition in biomedicine via instruction tuning of large language models
Keloth V, Hu Y, Xie Q, Peng X, Wang Y, Zheng A, Selek M, Raja K, Wei C, Jin Q, Lu Z, Chen Q, Xu H. Advancing entity recognition in biomedicine via instruction tuning of large language models. Bioinformatics 2024, 40: btae163. PMID: 38514400, PMCID: PMC11001490, DOI: 10.1093/bioinformatics/btae163.Peer-Reviewed Original ResearchNamed Entity RecognitionSequence labeling taskNatural language processingBiomedical NER datasetsLanguage modelNER datasetsEntity recognitionLabeling taskText generationField of natural language processingBiomedical NERFew-shot learning capabilityReasoning tasksMulti-domain scenariosDomain-specific modelsEnd-to-endMinimal fine-tuningSOTA performanceF1 scoreHealthcare applicationsBiomedical entitiesBiomedical domainLanguage processingMulti-taskingPubMedBERT model
2020
Named Entity Recognition from Table Headers in Randomized Controlled Trial Articles
Wei Q, Zhou Y, Zhao B, Hu X, Mei Q, Tao C, Xu H. Named Entity Recognition from Table Headers in Randomized Controlled Trial Articles. 2020, 00: 1-2. DOI: 10.1109/ichi48887.2020.9374323.Peer-Reviewed Original ResearchTable headersEntity recognitionDeep learning-based approachBiomedical text miningLearning-based approachNamed Entity RecognitionInformation extractionBiomedical entitiesF1 scoreText miningUnstructured natureBiomedical articlesContextual informationComputational applicationsHeaderSemantic complexityBetter performanceCorpusRecognitionInformationMiningApplicationsImportant informationComplexityBiomedical research
2018
Combine Factual Medical Knowledge and Distributed Word Representation to Improve Clinical Named Entity Recognition.
Wu Y, Yang X, Bian J, Guo Y, Xu H, Hogan W. Combine Factual Medical Knowledge and Distributed Word Representation to Improve Clinical Named Entity Recognition. AMIA Annual Symposium Proceedings 2018, 2018: 1110-1117. PMID: 30815153, PMCID: PMC6371322.Peer-Reviewed Original ResearchConceptsRecurrent neural networkWord embeddingsOne-hot vectorsWord representationsLow-frequency wordsOnly word embeddingsClinical Named Entity RecognitionClinical NER tasksWord embedding methodsConditional Random FieldsStatistical language modelNamed Entity RecognitionUnlabeled corpusLanguage modelLanguage systemNER taskDecent representationFactual medical knowledgeImportant wordsDeep learning modelsEntity recognitionClinical corpusNamed Entity Recognition SystemArt performanceFeature representationClinical Named Entity Recognition Using Deep Learning Models.
Wu Y, Jiang M, Xu J, Zhi D, Xu H. Clinical Named Entity Recognition Using Deep Learning Models. AMIA Annual Symposium Proceedings 2018, 2017: 1812-1819. PMID: 29854252, PMCID: PMC5977567.Peer-Reviewed Original ResearchConceptsClinical Named Entity RecognitionNamed Entity RecognitionDeep learning modelsConvolutional neural networkClinical NER systemRecurrent neural networkNeural networkLearning modelEntity recognitionRNN modelNER systemDeep neural network architecturePopular deep learning architecturesNatural language processing tasksUnsupervised learning featuresConditional random field modelAutomatic feature learningDeep learning architectureClinical NER tasksDeep neural networksNeural network architectureClinical concept extractionLanguage processing tasksFeature learningLearning architecture
2017
Knowledge-Based Approach for Named Entity Recognition in Biomedical Literature: A Use Case in Biomedical Software Identification
Amith M, Zhang Y, Xu H, Tao C. Knowledge-Based Approach for Named Entity Recognition in Biomedical Literature: A Use Case in Biomedical Software Identification. Lecture Notes In Computer Science 2017, 10351: 386-395. DOI: 10.1007/978-3-319-60045-1_40.Peer-Reviewed Original ResearchEntity recognitionNatural language processingContextual semantic informationNamed Entity RecognitionEntity recognition methodFeatures of ontologyMachine learning approachesKnowledge-based approachSoftware entitiesSoftware namesInformation extractionUse casesBiomedical softwareSemantic informationSoftware identificationLanguage processingRecognition methodLearning approachBiomedical literatureRecognitionOntologyEntitiesSoftwareResearch abstractsTask
2016
Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning
Zhang Y, Xu J, Chen H, Wang J, Wu Y, Prakasam M, Xu H. Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning. Database 2016, 2016: baw049. PMID: 27087307, PMCID: PMC4834204, DOI: 10.1093/database/baw049.Peer-Reviewed Original ResearchConceptsMachine learning-based systemsLearning-based systemConditional Random FieldsDomain knowledgeEntity recognitionMatthews correlation coefficientDrug Named Entity RecognitionBioCreative V challengeInformation extraction systemWord representation featuresUnsupervised feature learningUnsupervised learning algorithmNamed Entity RecognitionSemantic type informationSupport vector machinePrecision-recall curveBrown clusteringFeature learningFeature engineeringUnsupervised featureIndividual subtasksMining systemNER taskLearning algorithmCPD task
2015
A Study of Neural Word Embeddings for Named Entity Recognition in Clinical Text.
Wu Y, Xu J, Jiang M, Zhang Y, Xu H. A Study of Neural Word Embeddings for Named Entity Recognition in Clinical Text. AMIA Annual Symposium Proceedings 2015, 2015: 1326-33. PMID: 26958273, PMCID: PMC4765694.Peer-Reviewed Original ResearchConceptsNamed Entity RecognitionClinical NER systemNeural word embeddingsClinical Named Entity RecognitionWord embeddingsNER systemWord representationsI2b2 dataEntity recognitionEmbedding featuresClinical textNatural language processing researchConditional Random FieldsLanguage processing researchWord embedding featuresLarge unlabeled corpusBrown clustersNeural wordImportant patient informationFeature representationF1 scoreIntelligent monitoringCritical taskUnlabeled corpusSemantic relationsA comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature
Tang B, Feng Y, Wang X, Wu Y, Zhang Y, Jiang M, Wang J, Xu H. A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature. Journal Of Cheminformatics 2015, 7: s8. PMID: 25810779, PMCID: PMC4331698, DOI: 10.1186/1758-2946-7-s1-s8.Peer-Reviewed Original ResearchMachine learning-based systemsConditional Random FieldsLearning-based systemEntity recognition systemSupport vector machineEntity recognitionRecognition systemF-measureChallenge organizersDrug Named Entity RecognitionVector machineStructured support vector machineMicro F-measureInformation extraction tasksWord representation featuresNamed Entity RecognitionTest setRandom fieldsPrimary evaluation measureBrown clusteringDocument indexingIndividual subtasksExtraction taskRandom IndexingBiomedical domainNamed Entity Recognition in Chinese Clinical Text Using Deep Neural Network.
Wu Y, Jiang M, Lei J, Xu H. Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network. 2015, 216: 624-8. PMID: 26262126, PMCID: PMC4624324.Peer-Reviewed Original ResearchConceptsDeep neural networksLarge unlabeled corpusNamed Entity RecognitionWord embeddingsUnlabeled corpusUnsupervised learningEntity recognitionNeural networkNatural language processing technologyNovel deep learning methodLanguage processing technologyDeep learning methodsUnsupervised feature learningFeature engineering approachImportant healthcare informationChinese clinical textTypes of entitiesFeature learningNER taskClinical textLearning methodsClinical documentsCRF modelHealthcare informationFree text
2014
Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks
Tang B, Cao H, Wang X, Chen Q, Xu H. Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks. BioMed Research International 2014, 2014: 240403. PMID: 24729964, PMCID: PMC3963372, DOI: 10.1155/2014/240403.Peer-Reviewed Original ResearchConceptsBiomedical Named Entity RecognitionWord representationsNamed Entity Recognition (NER) taskMachine learning-based approachWord representation featuresNatural language processingLearning-based approachEntity recognition taskNamed Entity RecognitionCluster-based representationJNLPBA corpusEntity recognitionBiomedical domainF-measureLanguage processingRepresentation featuresWord embeddingsRecognition taskWR algorithmDistributional representationsTaskBetter performanceAlgorithmRepresentationDifferent types