由知識(shí)挖掘提升商務(wù)智能應(yīng)用(謝邦昌)(編輯修改稿)
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【文章內(nèi)容簡(jiǎn)介】 s? System knowledge not needed by those who supply the domain knowledge.161。 Cons:? Annotated training data, and lots of it, is needed.? Isn’t necessarily better or cheaper than handbuilt sol’n161。 Examples:? Riloff et al., AutoSlog, Soderland WHISK (UMass)。 Mooney et al. Rapier (UTexas)。 Ciravegna (Sheffield)? Learn lexiconsyntactic patterns from templatesTrainerDecoderModelLanguageInputAnswersAnswersLanguageInputText Analysis SpectrumEntity ExtractionTargeted Factsand EventsClassificationClusteringConceptIdentificationWhat is thisdocumentabout?Who didwhat towhom whenwhere, etc.Why is getting dimensional data so hard?Hank bought plastic explosives from Henry inTucson yesterday.Named Entity ExtractionPeople,Weapons,Vehicles,DatesNEREngineHankHenryPlastic explosivesTucson11/01/07FrameNetName Extraction via MMsTextSpeechRecognition ExtractorSpeech Entities NEModelsLocationsPersonsOrganizationsThe delegation, which included the mander of the . troops in Bosnia, Lt. Gen. Sir Michael Rose, went to the Serb stronghold of Pale, near Sarajevo, for talks with Bosnian Serb leader Radovan Karadzic.TrainingProgramtrainingsentences answersThe delegation, which included the mander of . troops inBosnia, Lt. Gen. SirMichael Rose, went to the Serb stronghold ofPale, near Sarajevo, for talks with Bosnian Serb leader Radovan Karadzic.An easy but successful HMM application:?Prior to 1997 no learning approach petitive with handbuilt rule systems?Since 1997 Statistical approaches (BBN (Bikel et al. 1997), NYU, MITRE, CMU/JustSystems) achieve stateoftheart performanceNER數(shù)據(jù)庫探勘作業(yè)流程決策參考決策參考決策建議決策建議自動(dòng)分群自動(dòng)分群自動(dòng)／專家分類自動(dòng)／專家分類 事件關(guān)連分析事件關(guān)連分析文檔庫文檔庫知識(shí)本體論知識(shí)本體論推論圖推論圖知識(shí)地圖知識(shí)地圖概念分群概念分群documentDocumentCollection{sun}{beach}Frequenttermset:{surf}{fun}{sun, beach}clusterC1C2C4C5C3 Clustering:{C1, C2, C4, C5}.Clustering Description:{surf, sun, beach, fun}.AnophelesFeedback as Model InterpolationConcept CDocument DResultsFeedback DocsF={d1, d2 , …, d n}GenerativemodelDivergenceminimization?=0Nofeedback?=1Fullfeedback非單調(diào)性資料 (Heterogeneous)TDRTDRTDRTDRTDR成千成萬的歷史紀(jì)錄巨量分析文件分群1000解決方案?jìng)€(gè)案庫Mooter科學(xué)人雜志 3月號(hào)文件數(shù)據(jù)分群Annotation and TaggingOnNovember 16, 2023, IBM announced it hadacquired Collation, a privately held panybased inRedwood City, California forundisclosed amount.Date AcquiringOrganization AcquisitionEvent AcquiredOrganizationPlace AmountText AnnotatorDate Organization Place AmountNov. 16 IBM Redwood City, CAUndisclosedOutput toRDBMSXMLoutputOn DateNovember 16, 2023/Date, ACQUIRING ORGIBM/ACQUIRING ORG announced it had ACQUISITION EVENTacquired/ACQUISITION EVENT ACQUIRED ORGCollation/ACQUIRED ORG, a privately held pany based in PLACERedwood City, California/PLACE for AMOUNTundisclosed/AMOUNT amount.Linguistic Concept Extractionfrom Customer Service Records Bagof“Words”extractionCstmrIDCustomerYellowIncHappyNotSwitchCellPhoneExpressionsextractionCstmrIDCustomerYellowIncswitchCellPhoneNothappyNamedEntitiesextractionCustomer?CRMtermCstmr?YellowInc?TelcoCompanyCellPhone?TelcoTermNothappySwitchEvents/SentimentExtractionCustomer(cstmr)?cellphone?unhappy(Negative)Switchto(NegativePredicate)?yellowinc(Competition)CombinedWithstructureddataDecisionMakingChurner?SpecialOfferKnowledgeInferenceInformationExtractionInformationRetrievalExtracting Information From Text161。 Structuring knowledge from text? tagging, pounds, grammatical analysis, ontological interpretation
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