03806nam 2200685 450 991081285290332120230617015231.03-11-091049-710.1515/9783110910490(CKB)3360000000338166(SSID)ssj0000849524(PQKBManifestationID)11498793(PQKBTitleCode)TC0000849524(PQKBWorkID)10812841(PQKB)10824655(MiAaPQ)EBC3049947(DE-B1597)56705(OCoLC)840442841(OCoLC)948656441(DE-B1597)9783110910490(Au-PeEL)EBL3049947(CaPaEBR)ebr11036000(CaONFJC)MIL805414(OCoLC)647045058(EXLCZ)99336000000033816620050325h20052005 uy| 0engurcnu||||||||txtccrA unified approach to nasality and voicing /by Kuniya NasukawaBerlin ;New York :Mouton de Gruyter,[2005]©20051 online resource (205 pages) illustrationsStudies in generative grammar ;65Bibliographic Level Mode of Issuance: Monograph3-11-018481-8 Includes bibliographical references (pages [163]-177) and indexes.Front matter --Abstract --Acknowledgements --Contents --Abbreviations and symbols --Chapter 1. Nasal-voice affinities --Chapter 2. Typological aspects of nasality and voicing --Chapter 3. The melodic architecture of nasality, voicing and prenasality --Chapter 4. An integrated approach to nasality and long-lead voicing --Chapter 5. Prenasalisation and nasalisation of voiced obstruents --Chapter 6. Assimilatory processes involving nasality and voicing --Chapter 7. Conclusion --Notes --References --Language index --Subject index --Author indexThis book makes an important contribution to the expanding body of work in generative phonology which aims to reduce the number of traditionally recognized melodic categories in order to achieve a greater degree of restrictiveness. By analyzing data from a large number of different languages, Nasukawa establishes a clear affinity between nasality and voicing, and demonstrates the advantages of treating these two properties as different phonetic manifestations of a single nasal-voice category. The choice of whether to interpret this category as voicing or nasality is determined by the active or inactive status of a complement tier; when active, this complement tier enhances the acoustic image of its head category and is interpreted as voicing. This study deepens our understanding of the typological relation between nasality and voicing, and sheds new light on a number of related agreement phenomena such as nasal harmony, postnasal voicing assimilation, voiced-obstruent voicing assimilation and spontaneous prenasalisation.Studies in generative grammar ;65.Nasality (Phonetics)Grammar, Comparative and generalVoiceGrammar, Comparative and generalComplementGrammar, Comparative and generalAgreementNasality (Phonetics)Grammar, Comparative and generalVoice.Grammar, Comparative and generalComplement.Grammar, Comparative and generalAgreement.414/.8ET 220rvkNasukawa Kuniya1967-1596439MiAaPQMiAaPQMiAaPQBOOK9910812852903321A unified approach to nasality and voicing4078586UNINA04297nam 22006375 450 991074450830332120251009082300.09783031391798(print)3031391799(print)9783031391798303139179910.1007/978-3-031-39179-8(MiAaPQ)EBC30742315(Au-PeEL)EBL30742315(OCoLC)1397575687(DE-He213)978-3-031-39179-8(PPN)272736333(CKB)28222999100041(EXLCZ)992822299910004120230913d2023 u| 0engur|n#|||a|||atxtrdacontentcrdamediacrrdacarrierNeuro Symbolic Reasoning and Learning /by Paulo Shakarian, Chitta Baral, Gerardo I. Simari, Bowen Xi, Lahari Pokala1st ed. 2023.Cham :Springer Nature Switzerland :Imprint: Springer,2023.1 online resource (xii, 119 pages) illustrationsSpringerBriefs in Computer Science,2191-5776Print version: Shakarian, Paulo Neuro Symbolic Reasoning and Learning Cham : Springer,c2023 9783031391781 Includes bibliographical references.Chapter1 New Ideas in Neuro Symbolic Reasoning and Learning -- Chapter2 Brief Introduction to Propositional Logic and Predicate Calculus -- Chapter3 Fuzzy and Annotated Logic for Neuro Symbolic Artificial Intelligence -- Chapter4 LTN: Logic Tensor Networks -- Chapter5 Neuro Symbolic Reasoning with Ontological Networks -- Chapter6 LNN: Logical Neural Networks -- Chapter7 NeurASP -- Chapter8 Neuro Symbolic Learning with Differentiable Inductive Logic Programming -- Chapter9 Understanding SATNet: Constraint Learning and Symbol Grounding -- Chapter10 Neuro Symbolic AI for Sequential Decision Making -- Chapter11 Neuro Symbolic Applications.This book provides a broad overview of the key results and frameworks for various NSAI tasks as well as discussing important application areas. This book also covers neuro symbolic reasoning frameworks such as LNN, LTN, and NeurASP and learning frameworks. This would include differential inductive logic programming, constraint learning and deep symbolic policy learning. Additionally, application areas such a visual question answering and natural language processing are discussed as well as topics such as verification of neural networks and symbol grounding. Detailed algorithmic descriptions, example logic programs, and an online supplement that includes instructional videos and slides provide thorough but concise coverage of this important area of AI. Neuro symbolic artificial intelligence (NSAI) encompasses the combination of deep neural networks with symbolic logic for reasoning and learning tasks. NSAI frameworks are now capable of embedding priorknowledge in deep learning architectures, guiding the learning process with logical constraints, providing symbolic explainability, and using gradient-based approaches to learn logical statements. Several approaches are seeing usage in various application areas. This book is designed for researchers and advanced-level students trying to understand the current landscape of NSAI research as well as those looking to apply NSAI research in areas such as natural language processing and visual question answering. Practitioners who specialize in employing machine learning and AI systems for operational use will find this book useful as well.SpringerBriefs in Computer Science,2191-5776Artificial intelligenceMachine learningArtificial IntelligenceMachine LearningArtificial intelligence.Machine learning.Artificial Intelligence.Machine Learning.006.31Shakarian Paulo791399Baral ChittaSimari Gerardo I.Xi BowenPokala LahariMiAaPQMiAaPQMiAaPQBOOK9910744508303321Neuro symbolic reasoning and learning3659084UNINA