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Mastering Machine Learning with Spark 2.x

You're reading from   Mastering Machine Learning with Spark 2.x Harness the potential of machine learning, through spark

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781785283451
Length 340 pages
Edition 1st Edition
Languages
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Authors (3):
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Alex Tellez Alex Tellez
Author Profile Icon Alex Tellez
Alex Tellez
Michal Malohlava Michal Malohlava
Author Profile Icon Michal Malohlava
Michal Malohlava
Max Pumperla Max Pumperla
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Max Pumperla
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Toc

Motivation of word vectors

Similar to the work we did in the previous chapter, traditional NLP approaches rely on converting individual words--which we created via tokenization--into a format that a computer algorithm can learn (that is, predicting the movie sentiment). Doing this required us to convert a single review of N tokens into a fixed representation by creating a TF-IDF matrix. In doing so, we did two important things behind the scenes:

  1. Individual words were assigned an integer ID (for example, a hash). For example, the word friend might be assigned to 39,584, while the word bestie might be assigned to 99,928,472. Cognitively, we know that friend is very similar to bestie; however, any notion of similarity is lost by converting these tokens into integer IDs.
  2. By converting each token into an integer ID, we consequently lose the context with which the token was used. This...
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