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Taming Text: How to Find, Organize, and Manipulate It

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Taming Text: How to Find, Organize, and Manipulate It Cover

 

Synopses & Reviews

Publisher Comments:

Summary

Taming Text, winner of the 2013 Jolt Awards for Productivity, is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. This book explores how to automatically organize text using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. The book guides you through examples illustrating each of these topics, as well as the foundations upon which they are built.

About this BookThere is so much text in our lives, we are practically drowningin it. Fortunately, there are innovative tools and techniquesfor managing unstructured information that can throw thesmart developer a much-needed lifeline. You'll find them in thisbook.

Taming Text is a practical, example-driven guide to working withtext in real applications. This book introduces you to useful techniques like full-text search, proper name recognition,clustering, tagging, information extraction, and summarization.You'll explore real use cases as you systematically absorb thefoundations upon which they are built.Written in a clear and concise style, this book avoids jargon, explainingthe subject in terms you can understand without a backgroundin statistics or natural language processing. Examples arein Java, but the concepts can be applied in any language.

Written for Java developers, the book requires no prior knowledge of GWT.

Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

Winner of 2013 Jolt Awards: The Best Books—one of five notable books every serious programmer should read.

What's Inside

  • When to use text-taming techniques
  • Important open-source libraries like Solr and Mahout
  • How to build text-processing applications
About the Authors

Grant Ingersoll is an engineer, speaker, and trainer, a Lucenecommitter, and a cofounder of the Mahout machine-learning project. Thomas Morton is the primary developer of OpenNLP and Maximum Entropy. Drew Farris is a technology consultant, software developer, and contributor to Mahout,Lucene, and Solr.

"Takes the mystery out of verycomplex processes."—From the Foreword by Liz Liddy, Dean, iSchool, Syracuse University

Table of Contents

  1. Getting started taming text
  2. Foundations of taming text
  3. Searching
  4. Fuzzy string matching
  5. Identifying people, places, and things
  6. Clustering text
  7. Classification, categorization, and tagging
  8. Building an example question answering system
  9. Untamed text: exploring the next frontier

Synopsis:

It is no secret that the world is drowning in text and data. This causes real problems for everyday users who need to make sense of all the information available, and for software engineers who want to make their text-based applications more useful and user-friendly. Whether building a search engine for a corporate website, automatically organizing email, or extracting important nuggets of information from the news, dealing with unstructured text can be daunting.

Taming Text is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. It explores how to automatically organize text, using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. This book gives examples illustrating each of these topics, as well as the foundations upon which they are built.

Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

About the Author

Grant Ingersoll is a founder of Lucid Imagination, developing search and natural language processing tools. Prior to Lucid Imagination, he was a Senior Software Engineer at the Center for Natural Language Processing at Syracuse University. At the Center and, previously, at MNIS-TextWise, Grant worked on a number of text processing applications involving information retrieval, question answering, clustering, summarization, and categorization. Grant is a committer, as well as a speaker and trainer, on the Apache Lucene Java project and a co-founder of the Apache Mahout machine-learning project. He holds a master's degree in computer science from Syracuse University and a bachelor's degree in mathematics and computer science from Amherst College.

Thomas Morton writes software and performs research in the area of text processing and machine learning. He has been the primary developer and maintainer of the OpenNLP text processing project and Maximum Entropy machine learning project for the last 5 years. He received his doctorate in Computer Science from the University of Pennsylvania in 2005, and has worked in several industry positions applying text processing and machine learning to enterprise class development efforts. Currently he works as a software architect for Comcast Interactive Media in Philadelphia.

Drew Farris is a professional software developer and technology consultant whose interests focus on large scale analytics, distributed computing and machine learning. Previously, he worked at TextWise where he implemented a wide variety of text exploration, management and retrieval applications combining natural language processing, classification and visualization techniques. He has contributed to a number of open source projects including Apache Mahout, Lucene and Solr, and holds a master's degree in Information Resource Management from Syracuse University's iSchool and a B.F.A in Computer Graphics.

Product Details

ISBN:
9781933988382
Author:
Ingersoll, Grant S.
Publisher:
Manning Publications
Author:
Farris, Andrew L.
Author:
Morton, Thomas S.
Subject:
Data processing
Subject:
General-General
Subject:
Andrew Farris;Apache;Apache Mahout;Apache Solr;Grant Ingersoll;Lucene;OpenNLP;Search;Tom Morton;clustering;information retrieval;k-means;named entity recognition;natural language processing;summarization;text mining;text processing
Subject:
Andrew Farris;Apache;Apache Mahout;Apache Solr;Grant Ingersoll;Lucene;OpenNLP;Search;Tom Morton;clustering;information retrieval;k-means;named entity recognition;natural language processing;summarization;text mining;text processing
Subject:
Andrew Farris;Apache;Apache Mahout;Apache Solr;Grant Ingersoll;Lucene;OpenNLP;Search;Tom Morton;clustering;information retrieval;k-means;named entity recognition;natural language processing;summarization;text mining;text processing
Subject:
Andrew Farris;Apache;Apache Mahout;Apache Solr;Grant Ingersoll;Lucene;OpenNLP;Search;Tom Morton;clustering;information retrieval;k-means;named entity recognition;natural language processing;summarization;text mining;text processing
Subject:
Andrew Farris;Apache;Apache Mahout;Apache Solr;Grant Ingersoll;Lucene;OpenNLP;Search;Tom Morton;clustering;information retrieval;k-means;named entity recognition;natural language processing;summarization;text mining;text processing
Subject:
Andrew Farris;Apache;Apache Mahout;Apache Solr;Grant Ingersoll;Lucene;OpenNLP;Search;Tom Morton;clustering;information retrieval;k-means;named entity recognition;natural language processing;summarization;text mining;text processing
Subject:
Andrew Farris;Apache;Apache Mahout;Apache Solr;Grant Ingersoll;Lucene;OpenNLP;Search;Tom Morton;clustering;information retrieval;k-means;named entity recognition;natural language processing;summarization;text mining;text processing
Subject:
Andrew Farris;Apache;Apache Mahout;Apache Solr;Grant Ingersoll;Lucene;OpenNLP;Search;Tom Morton;clustering;information retrieval;k-means;named entity recognition;natural language processing;summarization;text mining;text processing
Subject:
Andrew Farris;Apache;Apache Mahout;Apache Solr;Grant Ingersoll;Lucene;OpenNLP;Search;Tom Morton;clustering;information retrieval;k-means;named entity recognition;natural language processing;summarization;text mining;text processing
Subject:
Andrew Farris;Apache;Apache Mahout;Apache Solr;Grant Ingersoll;Lucene;OpenNLP;Search;Tom Morton;clustering;information retrieval;k-means;named entity recognition;natural language processing;summarization;text mining;text processing
Subject:
Andrew Farris;Apache;Apache Mahout;Apache Solr;Grant Ingersoll;Lucene;OpenNLP;Search;Tom Morton;clustering;information retrieval;k-means;named entity recognition;natural language processing;summarization;text mining;text processing
Edition Description:
Print PDF
Publication Date:
20130131
Binding:
TRADE PAPER
Language:
English
Pages:
320
Dimensions:
9.25 x 7.38 in

Related Subjects

Computers and Internet » Computer Languages » Java
Computers and Internet » Computers Reference » General
Computers and Internet » Database » Design
Computers and Internet » Database » General
Computers and Internet » Software Engineering » Programming and Languages
Computers and Internet » Software Engineering » Project Management
Computers and Internet » Software Engineering » Tools

Taming Text: How to Find, Organize, and Manipulate It New Trade Paper
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Product details 320 pages Manning Publications - English 9781933988382 Reviews:
"Synopsis" by ,

It is no secret that the world is drowning in text and data. This causes real problems for everyday users who need to make sense of all the information available, and for software engineers who want to make their text-based applications more useful and user-friendly. Whether building a search engine for a corporate website, automatically organizing email, or extracting important nuggets of information from the news, dealing with unstructured text can be daunting.

Taming Text is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. It explores how to automatically organize text, using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. This book gives examples illustrating each of these topics, as well as the foundations upon which they are built.

Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

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