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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 topic classification

In Chapter 2, Detecting Spam Emails, we learned how to classify incoming emails as either spam or ham. Undoubtedly, quarantining unwanted correspondence in the spam folder is an excellent feature for any email application. But is that enough? Still, ending up with a large number of emails stacked in your inbox can be an equally unpleasant situation. For this reason, creating personalized folders to accommodate each item and facilitate either responding to or archiving email threads is common. This necessity is even more acute for companies or large organizations that offer a generic contact account for external people. Hence, the appropriate personnel or department must process a large volume of incoming questions, announcements, offers, and complaints. This task can be cumbersome for a single person, while an automated system can check every email and decide who its recipient should be.

Besides emails, businesses deal with many other unstructured...

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