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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

Teaching Machines to Translate

The universal translator is a prominent yet imaginary device commonly encountered in many science fiction novels, films, and TV series. Star Trek, for example, long ago included the device in its screenplay to accommodate the unhindered translation of alien languages into the native language of the user. But unfortunately, a Star Trek-like device doesn’t exist yet, and the vision of a universal translator has not been realized. This shortcoming comes as no surprise, given human languages’ fluidity, inherent ambiguity, and flexibility. Nevertheless, the effort to teach machines to work as efficient translators is constant, with fascinating results in recent years.

This chapter seeks to present the different methods for machine translation and, at the same time, enhance your skillset with many standard techniques for NLP. The differences in the methods presented are an excellent opportunity to contrast the design philosophy of top-down...

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