LEADER 05276nam 22005415 450 001 9910299280503321 005 20200703004226.0 010 $a3-319-76629-5 024 7 $a10.1007/978-3-319-76629-4 035 $a(CKB)4100000004831875 035 $a(DE-He213)978-3-319-76629-4 035 $a(MiAaPQ)EBC5419336 035 $z(PPN)258872500 035 $a(PPN)229496857 035 $a(EXLCZ)994100000004831875 100 $a20180606d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTranslation, Brains and the Computer $eA Neurolinguistic Solution to Ambiguity and Complexity in Machine Translation /$fby Bernard Scott 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XVI, 241 p. 55 illus.) 225 1 $aMachine Translation: Technologies and Applications,$x2522-8021 ;$v2 311 $a3-319-76628-7 327 $a1 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. 330 $aThis book is about machine translation (MT) and the classic problems associated with this language technology. It examines the causes of these problems and, for linguistic, rule-based systems, attributes the cause to language?s ambiguity and complexity and their interplay in logic-driven processes. For non-linguistic, data-driven systems, the book attributes translation shortcomings to the very lack of linguistics. It then proposes a demonstrable way to relieve these drawbacks in the shape of a working translation model (Logos Model) that has taken its inspiration from key assumptions about psycholinguistic and neurolinguistic function. The book suggests that this brain-based mechanism is effective precisely because it bridges both linguistically driven and data-driven methodologies. It shows how simulation of this cerebral mechanism has freed this one MT model from the all-important, classic problem of complexity when coping with the ambiguities of language. Logos Model accomplishes this by a data-driven process that does not sacrifice linguistic knowledge, but that, like the brain, integrates linguistics within a data-driven process. As a consequence, the book suggests that the brain-like mechanism embedded in this model has the potential to contribute to further advances in machine translation in all its technological instantiations. 410 0$aMachine Translation: Technologies and Applications,$x2522-8021 ;$v2 606 $aNatural language processing (Computer science) 606 $aComputational linguistics 606 $aPsycholinguistics 606 $aNatural Language Processing (NLP)$3https://scigraph.springernature.com/ontologies/product-market-codes/I21040 606 $aComputational Linguistics$3https://scigraph.springernature.com/ontologies/product-market-codes/N22000 606 $aPsycholinguistics$3https://scigraph.springernature.com/ontologies/product-market-codes/N35000 615 0$aNatural language processing (Computer science) 615 0$aComputational linguistics. 615 0$aPsycholinguistics. 615 14$aNatural Language Processing (NLP). 615 24$aComputational Linguistics. 615 24$aPsycholinguistics. 676 $a006.35 700 $aScott$b Bernard$4aut$4http://id.loc.gov/vocabulary/relators/aut$0964388 906 $aBOOK 912 $a9910299280503321 996 $aTranslation, Brains and the Computer$92288992 997 $aUNINA