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基于词典学习和结构映射的语义解析技术研究.docx

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基于词典学习和结构映射的语义解析技术研究 Title:ResearchonSemanticParsingTechniquesBasedonLexicalLearningandStructuralMapping Abstract: Semanticparsingisafundamentaltaskinnaturallanguageprocessingthataimstoconvertnaturallanguagesentencesintoformalrepresentations,suchaslogicalformsorexecutablecode.Thispaperpresentsacomprehensivestudyonsemanticparsingtechniquesbasedonlexicallearningandstructuralmapping.Themainobjectiveistoexplorehowtoeffectivelylearnlexicalknowledgefromadictionaryandapplyitintheprocessofsemanticinterpretation.Additionally,weinvestigatevariousapproachesforstructuralmapping,whichiscrucialformappingnaturallanguageinputintothedesiredoutputrepresentation.Experimentalresultsdemonstratetheeffectivenessoftheproposedtechniquesinimprovingtheaccuracyandefficiencyofsemanticparsingsystems. 1.Introduction Semanticparsingplaysavitalroleinenablingmachinestounderstandhumanlanguageandcarryoutcomplextasks.However,thelackofastandardizedsemanticrepresentationandtheinherentambiguityofnaturallanguagemakesemanticparsingachallengingtask.Toaddresstheseissues,thisstudyfocusesonleveraginglexicallearningandstructuralmappingtechniquestoenhancetheperformanceofsemanticparsingsystems. 2.LexicalLearning 2.1Dictionary-basedLearning Akeyaspectofsemanticparsingistheacquisitionoflexicalknowledgefromadictionary.Weexploretechniquessuchaswordembeddinganddistributionalsemanticstorepresentwordsinacontinuousvectorspaceandcapturetheirsemanticmeanings.Furthermore,weinvestigatemethodsforincorporatingexternalknowledgeresources,suchasWordNet,toenrichthelexicalknowledgebase. 2.2BootstrappingandSelf-learning Toovercomethelimitationsofdictionary-basedlearning,wediscusstechniquesforbootstrappingandself-learning,wherethesystemautomaticallyexpandsitslexicalknowledgebyanalyzinglargeamountsofunlabeledtextdata.Thisprocessinvolvesleveragingstatisticalmodels,suchasprobabilistictopicmodelsordeeplearningapproaches,toinferthemeaningofunknownwords. 3.StructuralMapping 3.1AbstractSyntaxTrees Onecommonapproachforthestructuralmappingstep