结合BERT词嵌入和双向循环卷积神经网络的新闻文本分类研究-豆柴文库

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结合BERT词嵌入和双向循环卷积神经网络的新闻文本分类研究
Title:AHybridApproachofBERTWordEmbeddingsandBi-directionalConvolutionalNeuralNetworksforNewsTextClassification
Abstract:
Inrecentyears,thevastamountofnewsarticlesontheinternethasmadeitincreasinglychallengingtoextractusefulinsightsfromtextdata.Textclassificationisafundamentaltaskinnaturallanguageprocessing(NLP)thataimstoautomaticallyassignpredefinedcategoriestotextualdocuments.ThispaperproposesanovelapproachthatcombinesthepowerofBERTwordembeddingsandbi-directionalconvolutionalneuralnetworks(Bi-CNNs)fornewstextclassification.TheproposedmethodleveragesBERT'scontextualizedwordrepresentationstocapturethesemanticmeaningofwordswhileexploitingthespatialdependenciesbetweenwordswithBi-CNNs.Experimentalresultsdemonstratethattheproposedapproachoutperformsexistingmethodsintermsofaccuracyandprovidesapromisingsolutionfornewstextclassificationtasks.
1.Introduction
Withtheexponentialgrowthofdigitalcontent,thetaskoftextclassificationhasgainedsignificantattentioninvariousdomains,includingnewsanalysis,sentimentanalysis,andcustomerreviews,amongothers.Newstextclassificationreferstotheprocessofassigningpredefinedcategoriesorlabels,suchaspolitics,sports,business,andtechnology,tonewsarticlesautomatically.Thistaskiscrucialfororganizingandstructuringlargevolumesoftextualdataforinformationretrievalandanalysis.
2.RelatedWork
Thissectionreviewstherelatedworkintheareasofwordembeddings,BERT,andconvolutionalneuralnetworksfortextclassification.Ithighlightsthelimitationsofexistingmethodsandmotivatestheneedforahybridapproach.
3.Methodology
Inthissection,wepresenttheproposedhybridapproachthatcombinesBERTwordembeddingsandBi-CNNsfornewstextclassification.Firstly,weoutlinetheBERTarchitectureanddescribehowitgeneratescontextualizedwordembeddings.Next,weintroducetheBi-CNNframework,whichenablesthemodeltocapturespatialdependenciesbetweenwords.Finally,weexplaintheintegrationofBERTandBi-CNNsanddetailthetrainingprocess.
4.ExperimentSetup
Weprovidedetailsaboutthedatasetusedforevaluati