05276nam 22005415 450 991029928050332120200703004226.03-319-76629-510.1007/978-3-319-76629-4(CKB)4100000004831875(DE-He213)978-3-319-76629-4(MiAaPQ)EBC5419336(PPN)258872500(PPN)229496857(EXLCZ)99410000000483187520180606d2018 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierTranslation, Brains and the Computer A Neurolinguistic Solution to Ambiguity and Complexity in Machine Translation /by Bernard Scott1st ed. 2018.Cham :Springer International Publishing :Imprint: Springer,2018.1 online resource (XVI, 241 p. 55 illus.) Machine Translation: Technologies and Applications,2522-8021 ;23-319-76628-7 1 Introduction -- 2 Background -- Logos Model Beginnings -- Advent of Statistical MT -- Overview of Logos Model Translation Process -- Psycholinguistic and Neurolinguistic Assumptions -- On Language and Grammar -- Conclusion -- 3 – Language and Ambiguity: Psycholinguistic Perspectives -- Levels of Ambiguity -- Language Acquisition and Translation -- Psycholinguistic Bases of Language Skills -- Practical Implications for Machine Translation -- Psycholinguistics in a Machine -- Conclusion -- 4– Language and Complexity: Neurolinguistic Perspectives -- Cognitive Complexity -- A Role for Semantic Abstraction -- Connectionism and Brain Simulation -- Logos Model as a Neural Network -- Language Processing in the Brain -- MT Performance and Underlying Competence -- Conclusion -- 5 – Syntax and Semantics: Dichotomy or Integration? -- Syntax versus Semantics: Is There a Third, Semantico- Syntactic Perspective? -- Recent Views of the Cerebral Process -- Syntax and Semantics: How Do They Relate? -- Conclusion -- 6 –Logos Model: Design and Performance -- The Translation Problem -- How Do You Represent Natural Language? -- How Do You Store Linguistic Knowledge? -- How Do You Apply Stored Knowledge To The Input Stream? -- How do you Effect Target Transfer and Generation? -- How Do You Deal with Complexity Issues? -- Conclusion -- 7 – Some limits on Translation Quality -- First Example -- Second Example -- Other Translation Examples -- Balancing the Picture -- Conclusion -- 8 – Deep Learning MT and Logos Model -- Points of Similarity and Differences -- Deep Learning, Logos Model and the Brain -- On Learning -- The Hippocampus Again -- Conclusion -- Part II -- The SAL Representation Language -- SAL Nouns -- SAL Verbs -- SAL Adjectives -- SAL Adverbs.This book is about machine translation (MT) and the classic problems associated with this language technology. It examines the causes of these problems and, for linguistic, rule-based systems, attributes the cause to language’s ambiguity and complexity and their interplay in logic-driven processes. For non-linguistic, data-driven systems, the book attributes translation shortcomings to the very lack of linguistics. It then proposes a demonstrable way to relieve these drawbacks in the shape of a working translation model (Logos Model) that has taken its inspiration from key assumptions about psycholinguistic and neurolinguistic function. The book suggests that this brain-based mechanism is effective precisely because it bridges both linguistically driven and data-driven methodologies. It shows how simulation of this cerebral mechanism has freed this one MT model from the all-important, classic problem of complexity when coping with the ambiguities of language. Logos Model accomplishes this by a data-driven process that does not sacrifice linguistic knowledge, but that, like the brain, integrates linguistics within a data-driven process. As a consequence, the book suggests that the brain-like mechanism embedded in this model has the potential to contribute to further advances in machine translation in all its technological instantiations.Machine Translation: Technologies and Applications,2522-8021 ;2Natural language processing (Computer science)Computational linguisticsPsycholinguisticsNatural Language Processing (NLP)https://scigraph.springernature.com/ontologies/product-market-codes/I21040Computational Linguisticshttps://scigraph.springernature.com/ontologies/product-market-codes/N22000Psycholinguisticshttps://scigraph.springernature.com/ontologies/product-market-codes/N35000Natural language processing (Computer science)Computational linguistics.Psycholinguistics.Natural Language Processing (NLP).Computational Linguistics.Psycholinguistics.006.35Scott Bernardauthttp://id.loc.gov/vocabulary/relators/aut964388BOOK9910299280503321Translation, Brains and the Computer2288992UNINA