Summary. Taming Text, winner of the Jolt Awards for Productivity, is a hands-on, example-driven guide to working with unstructured text in the context of. Text and the intelligent app: search and beyond Searching and matching 12 *. Extracting information. Grouping information 13 *. An intelligent. Taming Text Book Source Code. Contribute to tamingtext/book development by creating an account on GitHub.
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Taming Text, winner of the Jolt Awards for Productivity, is a hands-on, example-driven guide to working with unstructured text in the context of real- world. Taming Text is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. This book explores how to. lent, very pragmatic book, Taming Text, offering substantive, real-world, tested guid- ance and Microsoft Word, Adobe PDF, text, and a host of other types.
Taming Text , winner of the 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. 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.
We introduce the Apache Solr search server and show how to index content with it. Chapter 4 examines fuzzy string matching with prefixes and n-grams. We look at two character overlap measures—the Jaccard measure and the Jaro-Winkler dis- tance—and explain how to find candidate matches with Solr and rank them.
Chapter 5 presents the basic concepts behind named-entity recognition. We also cover how to customize OpenNLP entity identification for a new domain. Chapter 6 is devoted to clustering text. We also explain how to cluster whole document collections using Apache Mahout, and how to cluster search results using Carrot 2 Chapter 7 discusses the basic concepts behind classification, categorization, and tagging.
We show how categorization is used in text applications, and how to build, train, and evaluate classifiers using open source tools. We also use the Mahout imple- mentation of the naive Bayes algorithm to build a document categorizer. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.
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Sign in Sign up. One person found this helpful. Fantastic book and great example software. Good book for practical developers. See all 17 reviews. site Giveaway allows you to run promotional giveaways in order to create buzz, reward your audience, and attract new followers and customers.
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