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Mastering Spark for Data Science

You're reading from   Mastering Spark for Data Science Lightning fast and scalable data science solutions

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785882142
Length 560 pages
Edition 1st Edition
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Authors (5):
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David George David George
Author Profile Icon David George
David George
Matthew Hallett Matthew Hallett
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Matthew Hallett
Antoine Amend Antoine Amend
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Antoine Amend
Andrew Morgan Andrew Morgan
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Andrew Morgan
Albert Bifet Albert Bifet
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Albert Bifet
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Toc

Table of Contents (15) Chapters Close

Preface 1. The Big Data Science Ecosystem FREE CHAPTER 2. Data Acquisition 3. Input Formats and Schema 4. Exploratory Data Analysis 5. Spark for Geographic Analysis 6. Scraping Link-Based External Data 7. Building Communities 8. Building a Recommendation System 9. News Dictionary and Real-Time Tagging System 10. Story De-duplication and Mutation 11. Anomaly Detection on Sentiment Analysis 12. TrendCalculus 13. Secure Data 14. Scalable Algorithms

Detecting near duplicates

While this chapter is about grouping articles into stories, this first section is all about detecting near duplicates. Before delving into the de-duplication algorithm itself, it is worth introducing the notion of story and de-duplication in the context of news articles. Given two distinct articles - by distinct we mean two different URLs - we may observe the following scenarios:

  • The URL of article 1 actually redirects to article 2 or is an extension of the URL provided in article 2 (some additional URL parameters, for instance, or a shortened URL). Both articles with the same content are considered as true duplicates although their URLs are different.
  • Both article 1 and article 2 are covering the exact same event, but could have been written by two different publishers. They share lots of content in common, but are not truly similar. Based on certain rules explained hereafter, they might be considered as near-duplicates.
  • Both article 1 and article 2 are covering...
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