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 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 domain