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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

The machine learning paradigm

The essence behind computer programming is to dictate to machines how to perform laborious tasks quickly and without errors. Calculating the average value of a series of numbers, resizing a photograph, streaming a video clip, and many other tasks are well-defined processes that require sophisticated software to execute. When performing more complex tasks, however, providing all the execution steps is error-prone and can often lead to brittle and buggy programs. Unsurprisingly, regular updates of our favorite computer programs claim to fix various problems – until, of course, the next update.

In the last two decades, we are experiencing a strong paradigm shift in commercial software development based on ideas that have been available for several decades. Instead of explicitly defining all the execution steps for a program, we can give pairs of examples in the form of possible input and the desired output. In this configuration, the machine tries...

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