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dc.contributor.authorMcShane, Marjorie
dc.contributor.authorNirenburg, Sergei
dc.date.accessioned2022-02-21T15:13:20Z
dc.date.available2022-02-21T15:13:20Z
dc.date.issued2021
dc.identifierONIX_20220221_9780262363136_128
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/78608
dc.description.abstractA human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning—the deep, context-sensitive meaning that a person derives from spoken or written language. With Linguistics for the Age of AI, McShane and Nirenburg offer a roadmap for creating language-endowed intelligent agents (LEIAs) that can understand,explain, and learn. They describe the language-understanding capabilities of LEIAs from the perspectives of cognitive modeling and system building, emphasizing “actionability”—which involves achieving interpretations that are sufficiently deep, precise, and confident to support reasoning about action. After detailing their microtheories for topics such as semantic analysis, basic coreference, and situational reasoning, McShane and Nirenburg turn to agent applications developed using those microtheories and evaluations of a LEIA's language understanding capabilities. McShane and Nirenburg argue that the only way to achieve human-level language understanding by machines is to place linguistics front and center, using statistics and big data as contributing resources. They lay out a long-term research program that addresses linguistics and real-world reasoning together, within a comprehensive cognitive architecture.
dc.languageEnglish
dc.relation.ispartofseriesThe MIT Press
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learningen_US
dc.subject.classificationthema EDItEUR::C Language and Linguistics::CF Linguistics::CFM Lexicographyen_US
dc.subject.classificationthema EDItEUR::G Reference, Information and Interdisciplinary subjects::GT Interdisciplinary studies::GTK Cognitive studiesen_US
dc.subject.othernatural language understanding
dc.subject.othercomputational semantics
dc.subject.othercomputational pragmatics
dc.subject.othercomputational linguistics
dc.subject.otherintelligent agents
dc.subject.othercognitive modelling
dc.subject.othercognitive systems
dc.subject.otherAI
dc.subject.otherartificial intelligence
dc.subject.otherlanguage-endowed intelligent agents
dc.subject.othernatural language processing
dc.subject.otherNLP
dc.subject.otherlanguage-endowed intelligent agent systems
dc.subject.otherlinguistic and extralinguistic scope
dc.subject.otherunderstanding
dc.subject.otherExtracting and representing meaning
dc.subject.othertheories
dc.subject.othersystems and models
dc.subject.otheractionability
dc.subject.otherexplanation
dc.subject.otherTheory and methodology
dc.subject.otherknowledge bases
dc.subject.otherincrementality
dc.subject.othermicrotheories
dc.subject.otherPre-semantic analysis
dc.subject.othererror recovery
dc.subject.othermanaging complexity
dc.subject.otherModification
dc.subject.otherproposition-level semantic enhancements
dc.subject.otherconstructions
dc.subject.otherindirect speech acts
dc.subject.othernon-literal language
dc.subject.otherellipsis
dc.subject.otherfragments
dc.subject.otherunknown words
dc.subject.otherpersonal pronouns
dc.subject.otherbroad referring expressions
dc.subject.otherdefinite descriptions
dc.subject.otheranaphoric event coreference
dc.subject.otherResidual ambiguities
dc.subject.otherincongruities
dc.subject.otherunderspecification
dc.subject.otherincorporating
dc.subject.otherOntoAgent cognitive architecture
dc.subject.otherfractured syntax
dc.subject.othertreating underspecified elements
dc.subject.otherIntegrated NLU applications
dc.subject.otherMaryland Virtual Patient
dc.subject.othercognitive robotics
dc.subject.otherModel and system evaluation
dc.subject.othercomponent-level evaluation
dc.subject.otherholistic evaluation
dc.titleLinguistics for the Age of AI
dc.typebook
oapen.relation.isPublishedByae0cf962-f685-4933-93d1-916defa5123d
oapen.relation.isbn9780262363136
oapen.relation.isbn9780262045582
oapen.imprintThe MIT Press
oapen.pages448
oapen.place.publicationCambridge


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