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 Book

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

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