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Author Bhattacharyya, Pushpak, author.

Title Machine translation / Pushpak Bhattacharyya. [O'Reilly electronic resource]

Publication Info. Boca Raton : Taylor & Francis, A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Franck, Chapman and Hall/CRC, 2015.
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Available on O'Reilly for Public Libraries
Bookmark link: https://library.naperville-lib.org:444/record=b3171691~S1
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Description 1 online resource
Bibliography Includes bibliographical references and index.
Contents Cover; Dedication; Contents; List of Figures; List of Tables; Preface; Acknowledgments; About the Author; 1: Introduction; 2: Learning Bilingual Word Mappings; 3: IBM Model of Alignment; 4: Phrase-Based Machine Translation; 5: Rule-Based Machine Translation (RBMT); 6: Example-Based Machine Translation.
Summary List of FiguresList of TablesPrefaceAcknowledgmentsAbout the AuthorIntroductionA Feel for a Modern Approach to Machine Translation: Data-Driven MTMT Approaches: Vauquois TriangleUnderstanding Transfer over the Vauquois TriangleUnderstanding Ascending and Descending TransferLanguage Divergence with Illustration between Hindi and EnglishSyntactic DivergenceLexical-Semantic DivergenceThree Major Paradigms of Machine TranslationMT EvaluationAdequacy and FluencyAutomatic Evaluation of MT OutputSummaryFurther ReadingLearning Bilingual Word MappingsA Combinatorial ArgumentNecessary and Sufficient Con.
PhrasesAn Example to Illustrate Phrase Alignment TechniqueTwo-Way AlignmentsSymmetrizationExpansion of Aligned Words to PhrasesPhrase TableMathematics of Phrase-Based SMTUnderstanding Phrase-Based Translation through an ExampleDeriving Translation Model and Calculating Translation and Distortion ProbabilitiesGiving Different Weights to Model ParametersFixing λ Values: TuningDecodingExample to Illustrate DecodingMosesInstalling MosesWorkflow for Building a Phrase-Based SMT SystemPreprocessing for MosesTraining Language ModelTraining Phrase ModelTuningDecoding Test DataEvaluation MetricMore on MosesSummaryFurther ReadingRule-Based Machine Translation (RBMT)Two Kinds of RBMT: Interlingua and TransferWhat Exactly Is InterlinguaIllustration of Different Levels of TransferUniversal Networking Language (UNL)Illustration of UNLUNL Expressions as Binary PredicatesWhy UNLInterlingua and Word KnowledgeHow Universal Are UWsUWs and MultilingualityUWs and MultiwordsUW Dictionary and WordnetComparing and Contrasting UW Dictionary and WordnetTranslation Using InterlinguaIllustration of Analysis and GenerationDetails of English-to-UNL Conversion: With IllustrationIllustrated UNL GenerationUNL-to-Hindi Conversion: With IllustrationFunction Word InsertionCase Identification and Morphology GenerationRepresentative Rules for Function Words InsertionSyntax PlanningTransfer-Based MTWhat Exactly Are Transfer RulesCase Study of Marathi-Hindi Transfer-Based MTKrudant: The Crux of the Matter in M-H MTM-H MT SystemSummaryFurther ReadingExample-Based Machine TranslationIllustration of Essential Steps of EBMTDeeper Look at EBMT's WorkingWord MatchingMatching of HaveEBMT and Case-Based ReasoningText Similarity ComputationWord Based SimilarityTree and Graph Based SimilarityCBR's Similarity Computation Adapted to EBMTRecombination: Adaptation on Retrieved ExamplesBased on.
Subject Machine translating.
Translating and interpreting -- Data processing.
Traduction automatique.
Machine translating
Translating and interpreting -- Data processing
Other Form: Print version: Bhattacharyya, Pushpak. Machine translation. 9781439897188 1439897182 (DLC) 2014024895
ISBN 1439897190 (electronic bk.)
9781439897195 (electronic bk.)
(hardback ; acid-free paper)
(hardback ; acid-free paper)
9781439897201 (VitalBook ebook)
1439897204
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