000 | 05520nam a22005655i 4500 | ||
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001 | 978-3-319-76629-4 | ||
003 | DE-He213 | ||
005 | 20210201191437.0 | ||
007 | cr nn 008mamaa | ||
008 | 180606s2018 gw | s |||| 0|eng d | ||
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_a9783319766294 _9978-3-319-76629-4 |
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_a006.35 _223 |
100 | 1 |
_aScott, Bernard. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aTranslation, Brains and the Computer _h[electronic resource] : _bA Neurolinguistic Solution to Ambiguity and Complexity in Machine Translation / _cby Bernard Scott. |
250 | _a1st ed. 2018. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2018. |
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300 |
_aXVI, 241 p. 55 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aMachine Translation: Technologies and Applications, _x2522-8021 ; _v2 |
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500 | _aAcceso multiusuario | ||
505 | 0 | _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. | |
520 | _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. | ||
541 |
_fUABC ; _cTemporal ; _d01/01/2021-12/31/2023. |
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650 | 0 | _aNatural language processing (Computer science). | |
650 | 0 | _aComputational linguistics. | |
650 | 0 | _aPsycholinguistics. | |
650 | 1 | 4 |
_aNatural Language Processing (NLP). _0https://scigraph.springernature.com/ontologies/product-market-codes/I21040 |
650 | 2 | 4 |
_aComputational Linguistics. _0https://scigraph.springernature.com/ontologies/product-market-codes/N22000 |
650 | 2 | 4 |
_aPsycholinguistics. _0https://scigraph.springernature.com/ontologies/product-market-codes/N35000 |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783319766287 |
776 | 0 | 8 |
_iPrinted edition: _z9783319766300 |
776 | 0 | 8 |
_iPrinted edition: _z9783030095383 |
830 | 0 |
_aMachine Translation: Technologies and Applications, _x2522-8021 ; _v2 |
|
856 | 4 | 0 |
_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-76629-4 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
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