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i Data-Intensive Text Processing with MapReduce

IData-Intensive Text Processingwith MapReduceJimmy Lin and Chris DyerUniversity of Maryland, College ParkManuscript prepared April 11, 2010 This is the pre-production manuscript of a book in the Morgan & Claypool SynthesisLectures on Human Language Technologies. Anticipated publication date is .. ii1 Introduction .. in the Clouds .. Ideas .. Is This Different? .. This Book Is Not .. 172 MapReduce Basics .. Programming Roots .. and Reducers .. Execution Framework .. and Combiners .. Distributed File System .. Cluster Architecture .. 383 MapReduce Algorithm Design .. Aggregation .. Combiners and In-Mapper Algorithmic Correctness with Local and Stripes .. Relative Frequencies .. Sorting .. Joins .. Reduce-Side Map-Side Memory-Backed Join67 CONTENTS .. 684 Inverted Indexing for Text Retrieval .. Crawling .. Indexes .. Indexing: Baseline Implementation .. Indexing: Revised Implementation.

with processing large amounts of text, but touches on other types of data as well (e.g., relational and graph data). The problems and solutions we discuss mostly fall into the disciplinary boundaries of natural language processing (NLP) and information retrieval (IR). Recent work in these elds is dominated by a data-driven, empirical approach,

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