• Produktbild: Translation, Brains and the Computer
  • Produktbild: Translation, Brains and the Computer
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Translation, Brains and the Computer A Neurolinguistic Solution to Ambiguity and Complexity in Machine Translation

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

28.12.2018

Abbildungen

XVI, 55 illus., schwarz-weiss Illustrationen

Verlag

Springer

Seitenzahl

241

Maße (L/B/H)

23,5/15,5/1,5 cm

Gewicht

400 g

Auflage

Softcover reprint of the original 1st ed. 2018

Sprache

Englisch

ISBN

978-3-030-09538-3

Beschreibung

Rezension

“Natural language processing is one of the most rapidly evolving areas of artificial intelligence, and is the subject of this excellent book. … One of the important contributions of this valuable resource is its presentation and comparison of many current state-of-the-art machine translation systems available to the general public. Summing Up: Recommended. Advanced undergraduates through faculty and professionals.” (J. Brzezinski, Choice, Vol. 56 (6), February, 2019)


Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

28.12.2018

Abbildungen

XVI, 55 illus., schwarz-weiss Illustrationen

Verlag

Springer

Seitenzahl

241

Maße (L/B/H)

23,5/15,5/1,5 cm

Gewicht

400 g

Auflage

Softcover reprint of the original 1st ed. 2018

Sprache

Englisch

ISBN

978-3-030-09538-3

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Translation, Brains and the Computer
  • Produktbild: Translation, Brains and the Computer
  • 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.