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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 summarization

With the burden of a busy daily schedule, we all seek to reduce the time spent reading text data. Take a moment to contemplate the number of emails, reports, news articles, tweets, blog posts, and so on you confront in 24 hours. The human brain employs different strategies to compensate for this challenge, such as skipping sentences in the text or searching for specific keywords before focusing on the content. Many studies have examined this phenomenon, and one of the most cited ones refers to how people in the west read the content of a web page. Using eye-tracking techniques, researchers from the Nielsen Norman Group (https://www.nngroup.com/articles/f-shaped-pattern-reading-web-content-discovered/) showed that humans follow a reading pattern resembling the letter F, as illustrated in Figure 7.1:

Figure 7.1 – F-shaped reading pattern of a web page

The reading usually starts at the upper part of the content area (point...

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