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  • Book Overview & Buying Turning Text into Gold: Taxonomies and Textual Analytics
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Turning Text into Gold: Taxonomies and Textual Analytics

Turning Text into Gold: Taxonomies and Textual Analytics

By : Bill Inmon
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Turning Text into Gold: Taxonomies and Textual Analytics

Turning Text into Gold: Taxonomies and Textual Analytics

4.3 (4)
By: Bill Inmon

Overview of this book

With businesses operating round the clock, a large amount of data gets generated. This data can be efficiently converted into useful knowledge that can take your business to a higher level. This book introduces you to the concept of taxonomies and how they are used to simplify and understand the text. You'll explore how to use taxonomies for textual analytics. It begins with a quick history of taxonomies and their earliest usage. You’ll learn about the different types of taxonomies (recursive, networked, hierarchical, and so on). You'll also learn about ontologies and understand how the ontology becomes a bridge between the worlds of technology and business and commerce. The later chapters of the book show how to find the taxonomies that you need for successful textual analytics, update your taxonomies to include the constantly-changing language, and extract meaningful information from raw text using different tools, such as textual disambiguation, document fracturing, and so on. By the end of this book, you’ll be able to utilize the various aspects of taxonomies for efficient textual analysis.
Table of Contents (20 chapters)
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1
Introduction
5
4: Ontologies
7
6: Changing Taxonomies
9
8: Taxonomies and Data Models
10
9: Types of Textual Data
17
16: Airline Analytics
18
Glossary
19
References
20
Index

Stop Word Processing

Stop word processing removes all designated “stop” words. The English language is full of words that are extraneous. We need these words for proper communication, but they really don’t have much (if any) bearing on what is being discussed. For that reason, these stop words are excised from the raw text. Here is an example of stop word processing:

Image317679.jpg

In the example, we see that the system has examined the text and has removed all the words that are on the stop word list. One reason to use stop word processing is to remove unnecessary words from the output database. This “paring down” of the text has many benefits. The largest benefit is to remove words that do not have any real value. By “paring down” the text, the remaining processing is streamlined.

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Tech Concepts
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Programming languages
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Turning Text into Gold: Taxonomies and Textual Analytics
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