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基于双向LSTM模型的中文命名实体识别.docx

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基于双向LSTM模型的中文命名实体识别 Title:NamedEntityRecognitioninChineseUsingBiLSTMModel Abstract: NamedEntityRecognition(NER)isacriticaltaskinnaturallanguageprocessing(NLP)thatinvolvesidentifyingandclassifyingnamedentitiessuchaspeople,organizations,locations,andotherspecifiedcategorieswithintextdocuments.ThispaperproposesaBiLSTM-basedapproachforNERinChinese,whichleveragesthepowerofBidirectionalLongShort-TermMemory(BiLSTM)networkstocapturecontextualinformationandachieveaccurateentityrecognition. 1.Introduction: NamedEntityRecognitionisessentialforvariousNLPapplicationssuchasinformationextraction,questionanswering,andmachinetranslation.ChineseNERposesuniquechallengesduetothelackofexplicitwordboundariesandalargenumberofhomonyms.Totacklethesechallenges,weproposeaBiLSTM-basedmodelthatcaneffectivelycapturesequentialdependenciesandcontextinformation. 2.RelatedWork: ThissectionprovidesanoverviewoftheexistingapproachestoChineseNER,includingrule-basedmethodsandmachinelearning-basedmethodssuchasConditionalRandomFields(CRF),HiddenMarkovModels(HMM),andSupportVectorMachines(SVM).WediscusstheirlimitationsandmotivationsforadoptingaBiLSTMmodel. 3.Methodology: Inthissection,wedescribetheproposedBiLSTM-basedmodelforChineseNER.ThemodelconsistsoftwolayersofLSTMnetworksrunninginbothforwardandbackwarddirections.Weexplainthearchitecturaldetails,includingwordembedding,featureextraction,andlabelprediction.Wealsodiscussthepre-processingsteps,suchaswordsegmentationandfeaturegeneration. 4.Dataset: WeutilizeaChineseNERdatasetfortrainingandevaluationpurposes.Thisdatasetcontainslabeledtextdocumentsannotatedwithnamedentitytags.Wedescribethedatacollectionprocess,annotationguidelines,andstatisticsofthedataset. 5.ExperimentalSetup: Weoutlinetheexperimentalsetup,includingthechoiceofevaluationmetrics,parametertuning,modeltraining,andevaluationmethodologies.Wesplitthedatasetintotraining,validation,andtestsets,andpresenttheconfigurationdetailsoftheBiLSTMmodel. 6.ResultsandDiscussion: Wepresenttheexperimentalresultsonth