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Villanova CSC 9010 - Natural Language Generation

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Natural Language GenerationSlide 2NLG – Application TypesCanned TextTemplate FillingSlide 6Phrase Based SystemsSlide 8Feature Based SystemsSlide 10CycSlide 12Knowledge BaseInference EngineNL Processing SubsystemSlide 16Slide 17Slide 18Research CycSlide 20The Problems with Meaningful GenerationSCIGenCommunications from ElsewhereOther ExamplesReferencesNatural Language GenerationEric ClarkCSC 9010-002Natural Language GenerationAn essay on Natural Language GenerationAre you sitting comfortably? I really, really like Natural Language Generation. Indispensable to homosapians today, it is yet to receive proper recognition for laying the foundations of democracy. It is estimated that that Natural Language Generation is thought about eight times every day by socialists, who form the last great hope for our civilzation. In the light of this I will break down the issues in order to give each of them the thought that they fully deserve.NLG – Application TypesCanned TextTemplate FillingPhrase Based SystemsFeature Based SystemsCanned TextDownload Complete“PC Load Letter”. What does that mean?Template FillingCongratulations Eric . You have been pre-approved for a low 6.9 % interest rate on your new Citibank Gold Plus Titanium Super Mastercard.Template FillingMail MergeSmartform.comAestivaPhrase Based SystemsGeneralized TemplatesPhrase Pattern matched at top level[SUBJECT VERB OBJECT][NP VP][Det Adj Noun]Etc.Phrase Based SystemsCreates sentences effectivelyLimited to one sentenceScalability limited by interrelationshipsFeature Based SystemsDictated by the characteristics of the sentence desiredPositive/NegativeInterrogative/DeclarativePast/PresentFeature Based SystemsSimplisticLanguage creation defined by the featuresNew distinctions are just added to the list of other featuresCycCreated by Cycorp, Inc.Austin, TX“The Cyc Knowledge Server is a very large, multi-contextual knowledge base and inference engine”CycKnowledge BaseInference EngineNL Processing SubsystemKnowledge BaseFactsRules of ThumbHeuristicsTermsAssertionsInference EngineLogical DeductionInference MechanismsBest-first searchNL Processing SubsystemLexiconSyntactic ParserSemantic InterpreterNL Processing SubsystemLexiconNL Processing SubsystemSyntactic Parser{:SENTENCE {:NP {:DETP {#$Determiner [the]}} {:N-BAR {#$SimpleNoun [man]}}} {:VP {#$Verb [saw]} {:NP {:DETP {#$Determiner [the]}} {:N-BAR {#$SimpleNoun [light]}} {:PP {#$Preposition [with]} {:NP {:DETP {#$Determiner [the]}} {:N-BAR {#$SimpleNoun [telescope]}}}}}}}}The man saw the light with the telescope.NL Processing SubsystemSemantic Interpreter “Mary believes that the blue hat is pretty“(#$believes :SUBJECT :CLAUSE)Research CycBased on OpenCycOpen SourceLinux BasedHas NLG capabilitiesHybrid Phrase/Feature BasedResearch Cyc“ResearchCyc is a version of the Cyc technology designed specifically for use by researchers”The Problems with Meaningful GenerationLimited to specific domainsNot ready for PrimetimeSome useful “dumb” applications are still availableSCIGenComputer Science Research Paper Generator“Our aim here is to maximize amusement, rather than coherence.”CSC 2180Communications from ElsewhereBad PoetryBand NamesPost ModernismOther ExamplesList of Work in NLGReferencesBateman, J. A. and Teich, E. (1995), `Selective information presentation in an integrated publication system: an application of genre-driven text generation', Information Processing and Management 31(5), 753-767.Bhattacharya, S, ‘Natural Language Generation’, accessed 2005 – available via Web: http://www.mla.iitkgp.ernet.in/~monojit/slides/NLG.pptUszkoreit, H ‘Language Generation’ accessed 2005 – available via Web: http://cslu.cse.ogi.edu/HLTsurvey/ch4node2.htmlNatural language generation : third international conference, INLG 2004, Brockenhurst, UK, July 14-16, 2004 : proceedings / Anja Belz, Roger Evans, Paul Piwek (eds.)ResearchCyc – Cycorp Inc., accessed 2005 – available via Web:


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Villanova CSC 9010 - Natural Language Generation

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