LINGUIST List 21.3014 Wed Jul 21 2010 Books: Computational Linguistics: Lin, Dyer‌, Hirst (Ed) Editor for this issue: Maria Moreno-Rollins <marialinguistlist.org> Links to the websites of all LINGUIST's supporting publishers are available at the end of this issue. Directory         1.    Andrea Descoteaux Hugg, Data-Intensive Text Processing with MapReduce: Lin, Dyer‌, Hirst (Ed) Message 1: Data-Intensive Text Processing with MapReduce: Lin, Dyer‌, Hirst (Ed) Date: 14-Jul-2010 From: Andrea Descoteaux Hugg <andreamorganclaypool.com> Subject: Data-Intensive Text Processing with MapReduce: Lin, Dyer‌, Hirst (Ed) E-mail this message to a friend Title: Data-Intensive Text Processing with MapReduce Series Title: Synthesis Lectures on Human Language Technologies Published: 2010 Publisher: Morgan & Claypool Publishers                 Book URL: http://www.morganclaypool.com/doi/abs/10.2200/S00274ED1V01Y201006HLT007 Author: Jimmy Lin‌ Author: Chris Dyer‌ Editor: Graeme Hirst Electronic: ISBN: 9781608453436 Pages: 177 Price: U.S. $ 30.00 Paperback: ISBN: 9781608453429 Pages: 177 Price: U.S. $ 40.00 Abstract: Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks Linguistic Field(s): Computational Linguistics Written In: English (eng ) See this book announcement on our website: http://linguistlist.org/pubs/books/get-book.cfm?BookID=49307 Read more issues|LINGUIST home page|Top of issue -------------------------- Major Supporters -------------------------- Brill http://www.brill.nl Cambridge Scholars Publishing http://www.c-s-p.org Cambridge University Press http://us.cambridge.org Cascadilla Press http://www.cascadilla.com/ Continuum International Publishing Group Ltd http://www.continuumbooks.com De Gruyter Mouton http://www.degruyter.com/mouton Edinburgh University Press http://www.eup.ed.ac.uk/ Elsevier Ltd http://www.elsevier.com/linguistics Emerald Group Publishing Limited http://www.emeraldinsight.com/ Equinox Publishing Ltd http://www.equinoxpub.com/ European Language Resources Association - ELRA http://www.elra.info. 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