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dc.contributor.authorPanesar, Kulvinder
dc.date.accessioned2020-10-07T15:51:28Z
dc.date.accessioned2020-10-27T10:07:29Z
dc.date.available2020-10-07T15:51:28Z
dc.date.available2020-10-27T10:07:29Z
dc.date.issued2020-03
dc.identifier.citationPanesar K (2020) Natural language processing (NLP) in Artificial Intelligence (AI): a functional linguistic perspective. In: Gouveia SS (Ed.) The age of Artificial Intelligence: an exploration. Delaware: Vernon Press.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18140
dc.descriptionYesen_US
dc.description.abstractThis chapter encapsulates the multi-disciplinary nature that facilitates NLP in AI and reports on a linguistically orientated conversational software agent (CSA) (Panesar 2017) framework sensitive to natural language processing (NLP), language in the agent environment. We present a novel computational approach of using the functional linguistic theory of Role and Reference Grammar (RRG) as the linguistic engine. Viewing language as action, utterances change the state of the world, and hence speakers and hearer’s mental state change as a result of these utterances. The plan-based method of discourse management (DM) using the BDI model architecture is deployed, to support a greater complexity of conversation. This CSA investigates the integration, intersection and interface of the language, knowledge, speech act constructions (SAC) as a grammatical object, and the sub-model of BDI and DM for NLP. We present an investigation into the intersection and interface between our linguistic and knowledge (belief base) models for both dialogue management and planning. The architecture has three-phase models: (1) a linguistic model based on RRG; (2) Agent Cognitive Model (ACM) with (a) knowledge representation model employing conceptual graphs (CGs) serialised to Resource Description Framework (RDF); (b) a planning model underpinned by BDI concepts and intentionality and rational interaction; and (3) a dialogue model employing common ground. Use of RRG as a linguistic engine for the CSA was successful. We identify the complexity of the semantic gap of internal representations with details of a conceptual bridging solution.en_US
dc.language.isoenen_US
dc.publisherVernon Press
dc.relation.isreferencedbyhttps://vernonpress.com/book/935en_US
dc.rights© 2020 by the Authors. This is a draft version of a chapter in the book The age of Artificial Intelligence: an exploration edited by Gouveia SS published in 2020 by Vernon Press, link:https://vernonpress.com/book/935.en_US
dc.subjectNatural language processing (NLP)en_US
dc.subjectArtificial Intelligence (AI)en_US
dc.subjectConversational software agent (CSA)en_US
dc.subjectFunctional linguistic theory - Role and Reference Grammar (RRG)en_US
dc.titleNatural language processing (NLP) in Artificial Intelligence (AI): a functional linguistic perspectiveen_US
dc.status.refereedYesen_US
dc.date.application2020-03
dc.typeBook chapteren_US
dc.type.versionAccepted manuscripten_US
dc.date.updated2020-10-07T14:51:37Z
refterms.dateFOA2020-10-27T10:08:09Z


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