LEADER 05583nam 2200709 a 450 001 9910966619403321 005 20240401180742.0 010 $a9789027271587 010 $a9027271585 035 $a(CKB)2550000001110834 035 $a(EBL)1394820 035 $a(SSID)ssj0000980759 035 $a(PQKBManifestationID)11533218 035 $a(PQKBTitleCode)TC0000980759 035 $a(PQKBWorkID)10969165 035 $a(PQKB)10070747 035 $a(MiAaPQ)EBC1394820 035 $a(Au-PeEL)EBL1394820 035 $a(CaPaEBR)ebr10746269 035 $a(CaONFJC)MIL510726 035 $a(OCoLC)856628047 035 $a(DE-B1597)721285 035 $a(DE-B1597)9789027271587 035 $a(EXLCZ)992550000001110834 100 $a20130526d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aCasebook in functional discourse grammar /$fedited by J. Lachlan Mackenzie, Hella Olbertz 205 $a1st ed. 210 $aAmsterdam $cJohn Benjamins Pub. Co.$d2013 215 $a1 online resource (323 p.) 225 0 $aStudies in Language Companion Series ;$v137 225 0$aStudies in language companion series,$x0165-7763 ;$vv. 137 300 $aDescription based upon print version of record. 311 08$a9789027206046 311 08$a902720604X 311 08$a9781299794757 311 08$a1299794750 320 $aIncludes bibliographical references and indexes. 327 $aCasebook in Functional Discourse Grammar; Editorial page; Title page; LCC data; Table of contents; Abbreviations; Introduction; References; A new approach to clausal constituent order; 1. Introduction; 2. Constituent ordering in FDG; 2.1 Introduction; 2.2 Templates; 2.3 Hierarchical ordering; 2.4 Configurational ordering; 3. Classical constituent order typology; 4. A new approach to constituent order typology; 5. An illustration; 5.1 Introduction; 5.2 Predicate-medial languages; 5.2.1 Introduction; 5.2.2 Dutch; 5.2.3 English; 5.2.4 Leti; 5.2.5 Summary; 5.3 Predicate-initial languages 327 $a5.3.1 Introduction5.3.2 Scottish Gaelic; 5.3.3 Tzotzil; 5.3.4 Kokota; 5.3.5 Summary; 6. Conclusion; References; Spatial adpositions between lexicon and grammar; 1. Introduction: The adposition; 2. Spatial adpositions, lexical and grammatical; 3. Justifying the lexical-grammatical distinction for English and other languages; 4. The Complex Locational Expression and the marking of the semantic category location; 5. The major adpositional constructions across the world's languages; 6. Conclusion; References; Conceptual representation and formulation; 1. Introduction 327 $a2. Outline of the Conceptual Component3. Representing information within the Conceptualizer; 4. Composition of the Conceptual Level Representation; 5. Formulation; 6. Conceptualization and formulation in possessive constructions; 7. Conceptualization and formulation in passive constructions; 8. Conclusion; Abbreviations; References; External possessors and related constructions in Functional Discourse Grammar; 1. Introduction; 2. Constraints on the indirect object external possessors in Dutch; 3. The Dutch indirect object external possessor in relation to other constructions 327 $a3.1 Onomasiological variation3.2 Semasiological variation; 4. The representation of the indirect object external possessor in FDG; 5. The representation of related constructions in FDG; 6. Conclusion; References; Time reference in English indirect speech; 1. Introduction; 2. Temporal reference: Locating situations in time; 3. Previous approaches to tense copying; 3.1 Comrie (1986); 3.2 Declerck (1988); 4. Functional discourse grammar; 5. The function of (not) copying tense; 6. Conclusions; References; Raising in Functional Discourse Grammar; 1. Introduction; 2. Types of raising 327 $a3. The pragmatic motivation of raising processes in Spanish3.1 Subject to subject raising (SRR) in Spanish; 3.1.1 SSR in discourse; 3.2 Subject-to-Object Raising (SOR) in Spanish; 4. A FDG analysis of raising; 4.1 Formal analysis; 4.2 Pragmatic analysis; 5. Conclusion; References; Objective and subjective deontic modal necessity in FDG - evidence from Spanish auxiliary expressions; 1. Introduction; 2. Modal auxiliaries in Spanish; 3. Objective and subjective deontic modality in FDG; 4. The scope of objective and subjective deontic modality; 5. Discussion and conclusion; 6. Summary and outlook 327 $aReferences 330 $aThe theory of FDG claims that deontic modality can be either participant-oriented or event-oriented, both distinctions forming part of the Representational Level. However, there is evidence from Spanish and a number of other languages that event-oriented deontic modality can be coded twice, with different values in one and the same State-of-Affairs. We will therefore distinguish between objective and subjective deontic modality, where the latter has scope over the former. On the basis of the ways in which the expressions of subjective and objective deontic modality interact with tense and othe 410 0$aStudies in Language Companion Series 606 $aFunctional discourse grammar 615 0$aFunctional discourse grammar. 676 $a415 701 $aMackenzie$b J. Lachlan$0610171 701 $aOlbertz$b Hella$f1953-$01802122 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910966619403321 996 $aCasebook in functional discourse grammar$94347672 997 $aUNINA LEADER 04808nam 22007215 450 001 9910996487103321 005 20250410135641.0 010 $a981-9638-37-2 024 7 $a10.1007/978-981-96-3837-6 035 $a(CKB)38337906900041 035 $a(DE-He213)978-981-96-3837-6 035 $a(MiAaPQ)EBC32005811 035 $a(Au-PeEL)EBL32005811 035 $a(OCoLC)1514649923 035 $a(EXLCZ)9938337906900041 100 $a20250410d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAI in Banking $ePractical Applications and Case Studies /$fby Liyu Shao, Qin Chen, Min He 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (XXII, 354 p. 264 illus., 7 illus. in color.) 311 08$a981-9638-36-4 327 $aPart I: Smart Marketing -- Chapter 1. Mobile Banking Potential Monthly Active Customer Mining: Automated Machine Learning Techniques -- Chapter 2. Retail Potential High-value Customer Identification: Graph Neural Network Technology -- Chapter 3. Accurate Recommendation for Banking: Recommender System -- Chapter 4. Assessing the Value of Bank Online Marketing Posts: Reinforcement Learning Techniques -- Chapter 5: Modeling Binary Causal Effects of Related Repayments: Causal Inference Techniques -- Part II: Intelligent Risk Control -- Chapter 6. Telecom Fraud Money Laundering Account Recognition Case: Multiple Machine Learning Techniques -- Chapter 7. Developing a Dialectal Speech Phone Collection Bimodal Robot from Scratch: Intelligent Voice Q&A Technology -- Chapter 8. Chattel Collateral Warehouse Visual Monitoring Project: Image Understanding Technology -- Chapter 9. Personal Loan Delinquency Prediction Project: Bayesian Network Techniques -- Part III: Intelligent Operation -- Chapter 10. Enterprise WeChat Private Traffic Customer Cold Start Program: Automated Control Technology -- Chapter 11 Intelligent Inspection Robot for Commercial Bank Data Centers: Computer Vision Technology. 330 $aBig data and artificial intelligence (AI) cannot remain limited to academic theoretical research. It is crucial to utilize them in practical business scenarios, enabling cutting-edge technology to generate tangible value. This book delves into the application of AI from theory to practice, offering detailed insights into AI project design and code implementation across eleven business scenarios in four major sectors: retail banking, e-banking, bank credit, and tech operations. It provides hands-on examples of various technologies, including automatic machine learning, integrated learning, graph computation, recommendation systems, causal inference, generative adversarial networks, supervised learning, unsupervised learning, computer vision, reinforcement learning, fuzzy control, automatic control, speech recognition, semantic understanding, Bayesian networks, edge computing, and more. This book stands as a rare and practical guide to AI projects in the banking industry. By avoiding complex mathematical formulas and theoretical analyses, it uses plain language to illustrate how to apply AI technology in commercial banking business scenarios. With its strong readability and practical approach, this book enables readers to swiftly develop their own AI projects. 606 $aMachine learning 606 $aArtificial intelligence$xData processing 606 $aComputer vision 606 $aNatural language processing (Computer science) 606 $aBiometric identification 606 $aPython (Computer program language) 606 $aMachine Learning 606 $aData Science 606 $aComputer Vision 606 $aNatural Language Processing (NLP) 606 $aBiometrics 606 $aPython 615 0$aMachine learning. 615 0$aArtificial intelligence$xData processing. 615 0$aComputer vision. 615 0$aNatural language processing (Computer science) 615 0$aBiometric identification. 615 0$aPython (Computer program language) 615 14$aMachine Learning. 615 24$aData Science. 615 24$aComputer Vision. 615 24$aNatural Language Processing (NLP). 615 24$aBiometrics. 615 24$aPython. 676 $a006.31 700 $aShao$b Liyu$4aut$4http://id.loc.gov/vocabulary/relators/aut$01817588 702 $aChen$b Qin$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aHe$b Min$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910996487103321 996 $aAI in Banking$94375476 997 $aUNINA