04417nam a22005175i 4500 978-3-031-57312-5 DE-He213 20251030112051.0 cr nn 008mamaa 240521s2024 sz | s |||| 0|eng d 9783031573125 978-3-031-57312-5 10.1007/978-3-031-57312-5 doi TA345-345.5 UN bicssc COM018000 bisacsh UN thema Hoya, Tetsuya. author. aut http://id.loc.gov/vocabulary/relators/aut 77141 Syntactic Networks—Kernel Memory Approach [electronic resource] / by Tetsuya Hoya. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XV, 129 p. 31 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Studies in Computational Intelligence, 1860-9503 ; 1157 Review 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. This 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. Engineering Data processing. Computational intelligence. 64052 Data Engineering. 66059 Computational Intelligence. 64052 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031573118 Printed edition: 9783031573132 Printed edition: 9783031573149 Studies in Computational Intelligence, 1860-9503 ; 1157 68854 https://doi.org/10.1007/978-3-031-57312-5 ZDB-2-INR ZDB-2-SXIT 60000 60000