Translation, Brains and the Computer A Neurolinguistic Solution to Ambiguity and Complexity in Machine Translation /

• Κύριος συγγραφέας: Scott, Bernard (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: Machine Translation: Technologies and Applications, 2
• Θέματα: Natural language processing (Computer science).; Computational linguistics.; Psycholinguistics.; Natural Language Processing (NLP).; Computational Linguistics.

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 proces...

Πίνακας περιεχομένων:

• 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.