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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

Summary

In this introductory chapter, we provided a high-level description of the themes covered in the book. First, we discussed different aspects of human language and what makes it such a unique resource. On the other hand, it can pose many challenges when processing human text, with ambiguity being the most serious threat.

Then, the discussion went into the current data explosion identifying the defining properties of big data. For AI, we presented its main types and the driving forces that led to its take-off. We also introduced the cutting-edge topics of ML, DL, and NLP. In this context, we set our own playground at the intersection of these fields.

A large part of the chapter was dedicated to the new paradigm shift in software programming imposed by ML. We also discussed the basic taxonomy of this emerging field. Finally, we concluded with the visualization and evaluation topics encountered many times throughout the book.

The next chapter deals with the first case study...

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