04040nam a22004335i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001600153072001500169072002300184072001400207100007600221245009100297250001800388264006900406300004300475336002600518337002600544338003600570347002400606490006200630505036200692520215601054650003403210650003203244650002203276650003203298710003403330773002603364776003603390776003603426776003603462830006203498856004603560978-3-031-57312-5DE-He21320251030112051.0cr nn 008mamaa240521s2024    sz |    s    |||| 0|eng d  a97830315731257 a10.1007/978-3-031-57312-52doi 4aTA345-345.5 7aUN2bicssc 7aCOM0180002bisacsh 7aUN2thema1 aHoya, Tetsuya.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aSyntactic Networks—Kernel Memory Approachh[electronic resource] /cby Tetsuya Hoya.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXV, 129 p. 31 illus.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aStudies in Computational Intelligence,x1860-9503 ;v11570 aReview of the Two Existing Artificial Neural Network Models – Multilayer Perceptron and Probabilistic Neural Networks -- Beyond the Original PNN Model – Kernel Memory for Modeling Various Neural Pattern Processing Mechanism -- Modules within the Artificial Mind System and Their Interactions Relevant to Language Pattern Processing -- Concept Formation.  aThis book proposes a novel connectionist approach to a challenging topic of language modeling within the context of kernel memory and artificial mind system, both proposed previously by the author in the very first volume of the series, Artificial Mind System—Kernel Memory Approach: Studies in Computational Intelligence, Vol. 1. The present volume focuses on how syntactic structures of language are modeled in terms of the respective composite connectionist architectures, each embracing both the nonsymbolic and symbolic parts. These two parts are developed via inter-module processes within the artificial mind system and eventually integrated under a unified framework of kernel memory. The data representation by the networks embodied within the kernel memory principle is essentially local, unlike conventional artificial neural network models such as the pervasive multilayer perceptron-based neural networks. With this locality principle, kernel memory inherently bears many attractive features, such as topologically unconstrained network formation, straightforward network growing, shrinking, and reconfiguration, no requirement of arduous iterative parameter tuning, construction of transparent and hierarchical data structures, and multimodal and temporal data processing via the network representation. Exploiting these multifacet properties of kernel memory with interweaving the notion of inter-module processing within the artificial mind system provides coherent accounts for concept formation and how various linguistic phenomena, viz. word compoundings, morphologies, and multiword constructions, are modeled. The description is then extended to more intricate network models of context-dependent lexical network and syntactic-oriented processing, the latter being the central theme of the present study, and further to those representing a hybrid of nonverbal and verbal thinking, and semantic and pragmatic aspects of sentential meaning. The book is intended for general readers engaging in various areas of study in cognitive science, computer science, engineering, linguistics, philosophy, psycholinguistics, and psychology. 0aEngineeringxData processing. 0aComputational intelligence.14aData Engineering.24aComputational Intelligence.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303157311808iPrinted edition:z978303157313208iPrinted edition:z9783031573149 0aStudies in Computational Intelligence,x1860-9503 ;v115740uhttps://doi.org/10.1007/978-3-031-57312-5