Data-Intensive Text Processing with MapReduce. Jimmy Lin
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Data-Intensive Text Processing with MapReduce
Synthesis Lectures on Human Language Technologies
Editor
Graeme Hirst, University of Toronto
Synthesis Lectures on Human Language Technologies is edited by Graeme Hirst of the University of Toronto. The series consists of 50- to 150-page monographs on topics relating to natural language processing, computational linguistics, information retrieval, and spoken language understanding. Emphasis is on important new techniques, on new applications, and on topics that combine two or more HLT subfields.
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Copyright © 2010 by Morgan & Claypool
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher.
Data-Intensive Text Processing with MapReduce
Jimmy Lin and Chris Dyer
ISBN: 9781608453429 paperback
ISBN: 9781608453436 ebook
DOI 10.2200/S00274ED1V01Y201006HLT007
A Publication in the Morgan & Claypool Publishers series
SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES
Lecture #7
Series Editor: Graeme Hirst, University of Toronto Series ISSN Synthesis Lectures on Human Language Technologies Print 1947-4040 Electronic 1947-4059
Data-Intensive Text Processing with MapReduce
Jimmy Lin and Chris Dyer
University of Maryland
SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES #7
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.
KEYWORDS
Hadoop, parallel and distributed programming, algorithm design, text processing, natural language processing, information retrieval, machine learning
Contents
2.1 Functional Programming Roots
2.4 Partitioners and Combiners
2.5 The Distributed File System
2.6 Hadoop Cluster Architecture
2.7 Summary
3.1 Local Aggregation
3.1.1 Combiners and In-Mapper Combining
3.1.2 Algorithmic Correctness with Local Aggregation
3.2 Pairs and Stripes
3.3 Computing Relative Frequencies
3.4 Secondary Sorting