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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

Understanding machine translation

A serious impediment to spreading new information, ideas, and knowledge is the language barriers imposed by the different languages spoken worldwide. Despite the cultural richness brought to our global heritage, they can pose significant hurdles to efficient human communication. This chapter focuses on machine translation (MT), which aims to alleviate these barriers. MT is the process of automatically converting a piece of text from a source into a target language without human intervention. This task is more than a modest goal and demands the synergy of various emerging fields to address the peculiarities of human language. For instance, with their inherent ambiguity and flexibility, you can expect multiple situations where more than one best translation exists. Despite many prominent MT systems appearing in recent years, the technology is not new. In the 50s, it was part of the first computing applications. Nevertheless, significant progress has...

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