Data-Intensive Text Processing with MapReduce. Jimmy Lin

Data-Intensive Text Processing with MapReduce

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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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Semantic Role Labeling

Martha Palmer, Daniel Gildea, and Nianwen Xue

2010

Spoken Dialogue Systems

Kristiina Jokinen and Michael McTear

2009

Introduction to Chinese Natural Language Processing

Kam-Fai Wong, Wenjie Li, Ruifeng Xu, and Zheng-sheng Zhang

2009

Introduction to Linguistic Annotation and Text Analytics

Graham Wilcock

2009

Dependency Parsing

Sandra Kübler, Ryan McDonald, and Joakim Nivre

2009

Statistical Language Models for Information Retrieval

ChengXiang Zhai

2008

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

www.morganclaypool.com

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

Acknowledgments

1 Introduction

1.1 Computing in the Clouds

1.2 Big Ideas

1.3 Why Is This Different?

1.4 What This Book Is Not

2 MapReduce Basics

2.1 Functional Programming Roots

2.2 Mappers and Reducers