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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

Understanding text generation

Our access to various services gradually evolves to become technology-driven rather than human-driven. Try to think of the last time you contacted the call center of a company, where an automated system probably answered your call. Replacing the human factor presents many competitive advantages in terms of cost and availability. However, these systems do not fully incorporate the communicative behaviors humans use and therefore are limited in reaching their full potential. The effort, in any case, is to create machines that are increasingly adept at sounding human and can pass the Turing test, which has long been a benchmark for machine intelligence.

Interesting fact

In 1950, the ingenious computer scientist Alan Turing introduced a test to check whether a machine can consistently fool an interviewer into believing it is a human. Today, the test refers to a more general behavioral benchmark for the presence of intelligence.

Natural language generation...

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