Machine translation: technologies and applications ;
Volume Designation
volume 2
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references.
CONTENTS NOTE
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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 -- On cognitive complexity -- A role for semantic abstraction and generalization -- 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 versus 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 cope with complexity? -- 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 and continual learning -- Conclusion -- Part II -- 9 The SAL representation language -- Overview of SAL -- SAL parts of speech -- SAL nouns (WC 1) -- SAL verbs (WC 2) -- SAL adjectives (WC 4) -- SAL Adverbs (WC 3 and WC 6).
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SUMMARY OR ABSTRACT
Text of Note
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.
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
Springer Nature
Stock Number
com.springer.onix.9783319766294
OTHER EDITION IN ANOTHER MEDIUM
Title
TRANSLATION, BRAINS AND THE COMPUTER.
International Standard Book Number
3319766287
TOPICAL NAME USED AS SUBJECT
Machine translating.
Neurolinguistics.
Psycholinguistics.
Translating and interpreting-- Data processing.
Computational linguistics.
FOREIGN LANGUAGE STUDY-- Multi-Language Phrasebooks.
LANGUAGE ARTS & DISCIPLINES-- Alphabets & Writing Systems.
LANGUAGE ARTS & DISCIPLINES-- Grammar & Punctuation.
LANGUAGE ARTS & DISCIPLINES-- Linguistics-- General.