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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

Evaluation of the results

Determining the value or worth of something in terms of quantity and quality is the process of evaluation. The increasing sophistication of text systems necessitates evaluation frameworks that measure the stated objectives and anticipated results. These frameworks serve a dual role – assessing different versions of the same product and also comparing similar systems. The topic of evaluation has grown into an essential part of systems development and a research field of its own.

Numerous convenient methods have been put forth to evaluate ML systems, which frequently make use of various computer- and human-centered metrics, most commonly known as objective and subjective evaluation. For example, using objective metrics allows us to measure something consistently and typically defies interpretation; either the spam detector achieved an accuracy above a threshold or didn’t. On the other hand, subjective evaluations are more expensive and time...

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