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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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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 Machine Learning for Text

The language phenomenon is still shrouded in mystery despite the recent achievements in various scientific disciplines in terms of understanding how and why it works. Yet, surprisingly, homo sapiens are the only species to develop this complex medium for exchanging information, which has led to the most striking accomplishments of humankind. Although the oral and gestural forms of language were the driving forces over millennia, their written counterpart decisively spread knowledge worldwide. Inspired by the expressive power of human texts, this introductory chapter sets the scene for the discussion in the following chapters, where we examine how to teach machines to extract meaningful interpretations from text corpora.

Building machines that learn from observations is becoming the dominant paradigm due to the ever-increasing amount of data that cannot be processed using traditional methods. For instance, text data is produced in vast quantities...

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