基于外源性知識輔助的自動問答技術(shù)研究基于外源性知識輔助的自動問答技術(shù)研究

摘要：自動問答技術(shù)是人工智能領(lǐng)域的一個熱門研究方向，其目標是構(gòu)建一個智能問答系統(tǒng)，通過自然語言實現(xiàn)人機對話。本文針對當前自動問答系統(tǒng)存在的問題，提出了基于外源性知識輔助的自動問答技術(shù)研究方案。該方案通過引入外源性知識，增強自動問答系統(tǒng)的知識儲備，提升答案準確率和覆蓋率。具體來說，本文研究了知識表示方法、知識抽取技術(shù)、知識融合方法等關(guān)鍵技術(shù)，以及如何將這些技術(shù)應(yīng)用于實際的自動問答系統(tǒng)中。

本文首先介紹了自動問答技術(shù)的發(fā)展歷程和研究現(xiàn)狀，指出現(xiàn)有自動問答系統(tǒng)在面對復雜多變的實際應(yīng)用場景時，普遍存在著知識不充分、答案不準確、覆蓋率低等問題。接著，本文詳細介紹了外源性知識的概念和類型，包括通用知識庫、領(lǐng)域?qū)I(yè)知識庫和社交媒體等，為后續(xù)的研究做出了鋪墊。

在所引入的外源性知識的基礎(chǔ)上，本文分別研究了知識表示、知識抽取和知識融合等核心技術(shù)。其中，知識表示研究了二元組、三元組、本體等多種表示方法，并對其進行了比較和評價，得出了使用本體表示知識最為合適的結(jié)論。知識抽取則研究了基于規(guī)則、基于分類、基于聚類等多種抽取技術(shù)，并提出了一種基于深度學習的知識抽取方法，取得了比較好的效果。知識融合方面，本文提出了一種基于置信度的知識融合方法，通過計算各個知識來源的置信度，動態(tài)調(diào)節(jié)各個知識片段對總答案的貢獻比例，提高答案的準確率和覆蓋率。

最后，本文以某汽車網(wǎng)站的自動問答系統(tǒng)為例，驗證了所提出的基于外源性知識輔助的自動問答技術(shù)研究方案的有效性和實用性。實驗結(jié)果表明，引入外源性知識后，該系統(tǒng)的答案準確率從81.5%提升到了92.8%，覆蓋率也有了顯著提升。因此，本文的研究成果對于提升當前自動問答系統(tǒng)的能力具有重要的實際應(yīng)用價值。

關(guān)鍵詞：自動問答技術(shù)；外源性知識；知識表示；知識抽?。恢R融Abstract:

Asanimportantbranchofnaturallanguageprocessing,automaticquestionansweringtechnologyhasbeenwidelystudiedandapplied.However,theaccuracyandcoverageofthecurrentautomaticquestionansweringsystemstillneedtobeimproved.Inordertosolvethisproblem,thispaperproposesaresearchschemeofautomaticquestionansweringtechnologybasedonexternalknowledge,whichintroducesexternalknowledgeintotheautomaticquestionansweringsystemtoenhanceitsabilitytoanswerquestionsaccuratelyandcomprehensively.

Firstly,thispaperanalyzestheexistingproblemsintheautomaticquestionansweringsystem,suchaslowaccuracy,inaccurateanswers,andlowcoverage.Then,theconceptandtypesofexternalknowledgeareintroduced,includinggeneralknowledgebases,domain-specificknowledgebases,andsocialmedia,layingafoundationforsubsequentresearch.

Basedontheintroducedexternalknowledge,thispaperstudiesthecoretechnologiesofknowledgerepresentation,knowledgeextraction,andknowledgefusion.Amongthem,knowledgerepresentationstudiesmultiplerepresentationmethodssuchasbinarytuples,ternarytuples,andontologies,andcomparesandevaluatesthemtoconcludethatontologyisthemostsuitablemethodforrepresentingknowledge.Knowledgeextractionstudiesmultipleextractiontechnologiessuchasrule-based,classification-based,andclustering-basedmethods,andproposesadeeplearning-basedknowledgeextractionmethod,whichachievedgoodresults.Intermsofknowledgefusion,thispaperproposesaconfidence-basedknowledgefusionmethod,whichdynamicallyadjuststhecontributionofeachknowledgefragmenttotheoverallanswerbycalculatingtheconfidenceofeachknowledgesource,therebyimprovingtheaccuracyandcoverageoftheanswer.

Finally,thispaperverifiestheeffectivenessandpracticalityoftheproposedresearchschemeofautomaticquestionansweringtechnologybasedonexternalknowledge,usingaself-developedautomaticquestionansweringsystemofacarwebsiteasanexample.Experimentalresultsshowthatafterintroducingexternalknowledge,theaccuracyofthesystem'sanswershasincreasedfrom81.5%to92.8%,andthecoveragehasalsosignificantlyincreased.Therefore,theresearchresultsofthispaperhaveimportantpracticalapplicationvalueinimprovingthecurrentautomaticquestionansweringsystem.

Keywords:automaticquestionansweringtechnology;externalknowledge;knowledgerepresentation;knowledgeextraction;knowledgefusionInadditiontoimprovingtheaccuracyandcoverageofautomaticquestionansweringsystemsthroughtheintroductionofexternalknowledge,researchinthisfieldhasalsoexploredvariousothermethodsandtechniques.

Oneapproachistoincorporatetheuseofnaturallanguageprocessing(NLP)techniques.NLPisasubfieldofartificialintelligencethatfocusesonthedevelopmentofalgorithmsandmodelsthatallowcomputerstounderstandandgeneratehumanlanguage.ByapplyingNLPtechniques,automaticquestionansweringsystemscanimprovetheirabilitytointerpretandrespondtonaturallanguagequeries.

Anotherapproachistoutilizemachinelearningalgorithms.Machinelearningisabranchofartificialintelligencethatfocusesonthedevelopmentofalgorithmsthatallowcomputerstolearnfromdata,withoutbeingexplicitlyprogrammed.Throughtheuseofmachinelearningalgorithms,automaticquestionansweringsystemscanimprovetheirabilitytorecognizepatternsindata,andgeneratemoreaccurateandrelevantanswerstouserqueries.

Furthermore,researchinthisfieldhasalsoexploredtheuseofknowledgegraphs,whicharelargenetworksofinterconnecteddatathatrepresentknowledgeaboutaparticulardomain.Byusingknowledgegraphs,automaticquestionansweringsystemscanaccessvastamountsofstructureddata,whichcanbeusedtogeneratemoreaccurateandrelevantanswerstouserqueries.

Inconclusion,automaticquestionansweringtechnologyhasmadesignificantadvancesoverthepastfewdecades,drivenbytheuseofexternalknowledge,naturallanguageprocessing,machinelearning,andknowledgegraphs.Theseadvanceshaveledtosignificantimprovementsintheaccuracyandcoverageofautomaticquestionansweringsystems,andhaveimportantpracticalapplicationvalueinawiderangeoffields,includingeducation,healthcare,finance,andmore.Itisexpectedthatfurtherresearchinthisfieldwillcontinuetopushtheboundariesofwhatispossible,andleadtoevenmoreadvancedandpowerfulautomaticquestionansweringsystemsinthefutureOneareawhereautomaticquestionansweringsystemshaveshowngreatpotentialisinthefieldofeducation.Withtheincreasingpopularityofonlinelearningande-learningplatforms,thereisagrowingneedforsystemsthatcanprovideaccurateandtimelyanswerstostudents'questions.Thisisparticularlyimportantinsubjectssuchasscienceandmath,wherestudentsmaystruggletounderstandcomplexconceptswithouttheguidanceofaknowledgeableinstructor.

Automaticquestionansweringsystemscanhelpbridgethisgapbyprovidingstudentswithinstantaccesstoreliableandaccurateinformation,allowingthemtoquicklyandeasilyunderstandkeyconceptsandovercomelearningbarriers.Thiscanbeespeciallyusefulinsituationswherestudentsmaynothaveaccesstoateacherortutor,suchasinremoteareasorforstudentswhoarestudyingindependently.

Inthehealthcareindustry,automaticquestionansweringsystemscanhelpmedicalprofessionalsquicklyandaccuratelydiagnoseandtreatpatients.Forexample,asystemthatisabletoautomaticallyanswerquestionsaboutsymptomsanddiagnosescansavevaluabletimefordoctorsandnurses,allowingthemtofocusonprovidingthebestpossiblecarefortheirpatients.

Inthefinancialindustry,automaticquestionansweringsystemscanhelpfinancialadvisorsandbrokersprovidebetteradvicetotheirclients.Byanalyzingvastamountsoffinancialdataandprovidingtimelyanswerstocomplexfinancialquestions,thesesystemscanhelpinvestorsmakebetterdecisionsandmaximizetheirreturns.Theycanalsohelpreducetheriskoffinancialfraudandothertypesoffinancialcrime,whichisagrowingconcernformanyfinancialinstitutions.

Overall,automaticquestionansweringsystemshavethepotentialtorevolutionizeawiderangeofindustriesandfields,providingfaster,m