Machine translation. (2015)

• Record Type: Book
• Further Information: Note: Pushpak Bhattacharyya.
• Authors: Bhattacharyya, Pushpak
• Contents: List of Figures List of Tables Preface Acknowledgments About the Author Introduction A Feel for a Modern Approach to Machine Translation: Data-Driven MT MT Approaches: Vauquois Triangle Understanding Transfer over the Vauquois Triangle Understanding Ascending and Descending Transfer Language Divergence with Illustration between Hindi and English Syntactic Divergence Lexical-Semantic Divergence Three Major Paradigms of Machine Translation MT Evaluation Adequacy and Fluency Automatic Evaluation of MT Output Summary Further Reading Learning Bilingual Word Mappings A Combinatorial Argument Necessary and Sufficient Conditions for Deterministic Alignment in Case of One-to-One Word Mapping A Naïve Estimate for Corpora Requirement Deeper Look at One-to-One Alignment Drawing Parallels with Part of Speech Tagging Heuristics-Based Computation of the VE × VF Table Iterative (EM-Based) Computation of the VE × VF Table Initialization and Iteration 1 of EM Iteration 2 Iteration 3 Mathematics of Alignment A Few Illustrative Problems to Clarify Application of EM Derivation of Alignment Probabilities Expressing the E- and M-Steps in Count Form Complexity Considerations Storage Time EM: Study of Progress in Parameter Values Necessity of at Least Two Sentences One-Same-Rest-Changed Situation One-Changed-Rest-Same Situation Summary Further Reading IBM Model of Alignment Factors Influencing P(f|e ) Alignment Factor a Length Factor m IBM Model 1 The Problem of Summation over Product in IBM Model 1 EM for Computing P(f|e) Alignment in a New Input Sentence Pair Translating a New Sentence in IBM Model 1: Decoding IBM Model 2 EM for Computing P(f|e) in IBM Model 2 Justification for and Linguistic Viability of P(i|j, l, m) IBM Model 3 Summary Further Reading Phrase-Based Machine Translation Need for Phrase Alignment Case of Promotional/Demotional Divergence Case of Multiword (Includes Idioms) Phrases Are Not Necessarily Linguistic Phrases An Example to Illustrate Phrase Alignment Technique Two-Way Alignments Symmetrization Expansion of Aligned Words to Phrases Phrase Table Mathematics of Phrase-Based SMT Understanding Phrase-Based Translation through an Example Deriving Translation Model and Calculating Translation and Distortion Probabilities Giving Different Weights to Model Parameters Fixing λ Values: Tuning Decoding Example to Illustrate Decoding Moses Installing Moses Workflow for Building a Phrase-Based SMT System Preprocessing for Moses Training Language Model Training Phrase Model Tuning Decoding Test Data Evaluation Metric More on Moses Summary Further Reading Rule-Based Machine Translation (RBMT) Two Kinds of RBMT: Interlingua and Transfer What Exactly Is Interlingua? Illustration of Different Levels of Transfer Universal Networking Language (UNL) Illustration of UNL UNL Expressions as Binary Predicates Why UNL? Interlingua and Word Knowledge How Universal Are UWs? UWs and Multilinguality UWs and Multiwords UW Dictionary and Wordnet Comparing and Contrasting UW Dictionary and Wordnet Translation Using Interlingua Illustration of Analysis and Generation Details of English-to-UNL Conversion: With Illustration Illustrated UNL Generation UNL-to-Hindi Conversion: With Illustration Function Word Insertion Case Identification and Morphology Generation Representative Rules for Function Words Insertion Syntax Planning Transfer-Based MT What Exactly Are Transfer Rules? Case Study of Marathi-Hindi Transfer-Based MT Krudant: The Crux of the Matter in M-H MT M-H MT System Summary Further Reading Example-Based Machine Translation Illustration of Essential Steps of EBMT Deeper Look at EBMT’s Working Word Matching Matching of Have EBMT and Case-Based Reasoning Text Similarity Computation Word Based Similarity Tree and Graph Based Similarity CBR’s Similarity Computation Adapted to EBMT Recombination: Adaptation on Retrieved Examples Based on Sentence Parts Based on Properties of Sentence Parts Recombination Using Parts of Semantic Graph EBMT and Translation Memory EBMT and SMT Summary Further Reading Index
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2015
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 418.020285; Machine translating
• Languages: English
• ISBNs: 9781439897195; 9781439897201
• Related ISBNs: 9781439897188
• Notes: Note: Description based on CIP data; item not viewed.
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