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Machine Learning Techniques for Text

You're reading from   Machine Learning Techniques for Text Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803242385
Length 448 pages
Edition 1st Edition
Languages
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Author (1):
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Nikos Tsourakis Nikos Tsourakis
Author Profile Icon Nikos Tsourakis
Nikos Tsourakis
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Toc

Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Introducing Machine Learning for Text 2. Chapter 2: Detecting Spam Emails FREE CHAPTER 3. Chapter 3: Classifying Topics of Newsgroup Posts 4. Chapter 4: Extracting Sentiments from Product Reviews 5. Chapter 5: Recommending Music Titles 6. Chapter 6: Teaching Machines to Translate 7. Chapter 7: Summarizing Wikipedia Articles 8. Chapter 8: Detecting Hateful and Offensive Language 9. Chapter 9: Generating Text in Chatbots 10. Chapter 10: Clustering Speech-to-Text Transcriptions 11. Index 12. Other Books You May Enjoy

Introducing linear regression

Before we delve into solving the main problem of this chapter, we need to provide the necessary theoretical framework. This section presents an ML technique purposely chosen to unfold the discussion and facilitate understanding of the methods that follow.

Let’s consider the three plots in Figure 4.11 that show the relationship between two variables: x and y. In this example, the opaque and transparent points correspond in one of two independent datasets:

Figure 4.11 – Variables with deterministic (A), statistical (B), and random relationship (C)

In Figure 4.11 (A), the points of both datasets reside on their line, which defines a clear deterministic relationship between the two variables. As x changes its value, we can precisely calculate the value of y using one of the line equations. In the middle plot, we cannot predict the exact value of y, but we can obtain a good approximation based again on the line equations...

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