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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

Basic Refinements

In the previous example, it would be wise to include the specific word preceded by a blank space in the search logic. For example, “ ford” instead of “ford”. Including the blank space would avoid retrieving the word “afford”.

Also note that the raw text is always searched as a lower case word. And any and all punctuations are removed. The raw text is searched as lower case in order to not misidentify a hit. For example, in the sentence “My Porsche runs fast.” the word “Porsche” needs to be identified as “Porsche”. Doing searches on a single case makes searching much more efficient. It has been observed that approximately 75% of raw text is served by these few basic refinements to taxonomy processing. The remaining 25% of text requires other techniques.

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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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