Format:
1 Online-Ressource (ix, 165 Seiten)
Edition:
Electronic reproduction Available via World Wide Web
ISBN:
9781608453436
Series Statement:
Synthesis lectures on human language technologies #7
Content:
Includes bibliographical references
Content:
1. Introduction -- Computing in the clouds -- Big ideas -- Why is this different -- What this book is not --
Content:
2. MapReduce basics -- Functional programming roots -- Mappers and reducers -- The execution framework -- Partitioners and combiners -- The distributed file system -- Hadoop cluster architecture -- Summary --
Content:
3. MapReduce algorithm design -- Local aggregation -- Combiners and in-mapper combining -- Algorithmic correctness with local aggregation -- Pairs and stripes -- Computing relative frequencies -- Secondary sorting -- Relational joins -- Reduce-side join -- Map-side join -- Memory-backed join -- Summary --
Content:
4. Inverted indexing for text retrieval -- Web crawling -- Inverted indexes -- Inverted indexing: baseline implementation -- Inverted indexing: revised implementation -- Index compression -- Byte-aligned and word-aligned codes -- Bit-aligned codes -- Postings compression -- What about retrieval -- Summary and additional readings --
Content:
5. Graph algorithms -- Graph representations -- Parallel breadth-first search -- PageRank -- Issues with graph processing -- Summary and additional readings --
Content:
6. EM algorithms for text processing -- Expectation maximization -- Maximum likelihood estimation -- A latent variable marble game -- MLE with latent variables -- Expectation maximization -- An EM example -- Hidden Markov models -- Three questions for hidden Markov models -- The forward algorithm -- The Viterbi algorithm -- Parameter estimation for HMMs -- Forward-backward training: summary -- EM in MapReduce -- HMM training in MapReduce -- Case study: word alignment for statistical machine translation -- Statistical phrase-based translation -- Brief digression: language modeling with MapReduce -- Word alignment -- Experiments -- EM-like algorithms -- Gradient-based optimization and log-linear models -- Summary and additional readings --
Content:
7. Closing remarks -- Limitations of MapReduce -- Alternative computing paradigms -- MapReduce and beyond --
Content:
Bibliography -- Authors' biographies
Note:
Includes bibliographical references
,
Acknowledgments; Introduction; Computing in the Clouds; Big Ideas; Why Is This Different?; What This Book Is Not; MapReduce Basics; Functional Programming Roots; Mappers and Reducers; The Execution Framework; Partitioners and Combiners; The Distributed File System; Hadoop Cluster Architecture; Summary; MapReduce Algorithm Design; Local Aggregation; Combiners and In-Mapper Combining; Algorithmic Correctness with Local Aggregation; Pairs and Stripes; Computing Relative Frequencies; Secondary Sorting; Relational Joins; Reduce-Side Join; Map-Side Join; Memory-Backed Join; Summary
,
Inverted Indexing for Text RetrievalWeb Crawling; Inverted Indexes; Inverted Indexing: Baseline Implementation; Inverted Indexing: Revised Implementation; Index Compression; Byte-Aligned and Word-Aligned Codes; Bit-Aligned Codes; Postings Compression; What About Retrieval?; Summary and Additional Readings; Graph Algorithms; Graph Representations; Parallel Breadth-First Search; PageRank; Issues with Graph Processing; Summary and Additional Readings; EM Algorithms for Text Processing; Expectation Maximization; Maximum Likelihood Estimation; A Latent Variable Marble Game
,
MLE with Latent VariablesExpectation Maximization; An EM Example; Hidden Markov Models; Three Questions for Hidden Markov Models; The Forward Algorithm; The Viterbi Algorithm; Parameter Estimation for HMMs; Forward-Backward Training: Summary; EM in MapReduce; HMM Training in MapReduce; Case Study: Word Alignment for Statistical Machine Translation; Statistical Phrase-Based Translation; Brief Digression: Language Modeling with MapReduce; Word Alignment; Experiments; EM-Like Algorithms; Gradient-Based Optimization and Log-Linear Models; Summary and Additional Readings; Closing Remarks
,
Limitations of MapReduceAlternative Computing Paradigms; MapReduce and Beyond; Bibliography; Authors' Biographies;
,
6. EM algorithms for text processing -- Expectation maximization -- Maximum likelihood estimation -- A latent variable marble game -- MLE with latent variables -- Expectation maximization -- An EM example -- Hidden Markov models -- Three questions for hidden Markov models -- The forward algorithm -- The Viterbi algorithm -- Parameter estimation for HMMs -- Forward-backward training: summary -- EM in MapReduce -- HMM training in MapReduce -- Case study: word alignment for statistical machine translation -- Statistical phrase-based translation -- Brief digression: language modeling with MapReduce -- Word alignment -- Experiments -- EM-like algorithms -- Gradient-based optimization and log-linear models -- Summary and additional readings
,
Electronic reproduction Available via World Wide Web
,
System requirements: Adobe Acrobat Reader.
,
Mode of access: World Wide Web.
Additional Edition:
ISBN 9781608453429
Additional Edition:
Erscheint auch als Druck-Ausgabe Data-Intensive Text Processing with MapReduce
Language:
English
Subjects:
Computer Science
Keywords:
Parallelverarbeitung
;
Hadoop
;
Verteiltes System
;
Natürlichsprachiges System
;
Information Retrieval
;
Maschinelles Lernen
DOI:
10.2200/S00274ED1V01Y201006HLT007
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