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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

Stemming

Another common form of linguistic disambiguity resolution is stemming. Stemming is the practice of reducing words to their Greek or Latin stems. For example, the words “moved”, “moving”, “mover”, “moves” all have the common word stem “mov”. Note that the “word stem” may or may not be an actual English word. Stemming is often useful in looking at text where the same word stem is found in many forms. Typically, conversations can best be analyzed by using stemming techniques.

Here is an example of stemming:

Image317727.jpg

In the example, the raw text contains the words “leaving” and “blessed”. The Latin word stems of these two words are “leav“ and “bless”.

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Turning Text into Gold: Taxonomies and Textual Analytics
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