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Mastering Elasticsearch 5.x

You're reading from   Mastering Elasticsearch 5.x Master the intricacies of Elasticsearch 5 and use it to create flexible and scalable search solutions

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786460189
Length 428 pages
Edition 3rd Edition
Languages
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Author (1):
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Bharvi Dixit Bharvi Dixit
Author Profile Icon Bharvi Dixit
Bharvi Dixit
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Table of Contents (13) Chapters Close

Preface 1. Revisiting Elasticsearch and the Changes FREE CHAPTER 2. The Improved Query DSL 3. Beyond Full Text Search 4. Data Modeling and Analytics 5. Improving the User Search Experience 6. The Index Distribution Architecture 7. Low-Level Index Control 8. Elasticsearch Administration 9. Data Transformation and Federated Search 10. Improving Performance 11. Developing Elasticsearch Plugins 12. Introducing Elastic Stack 5.0

Available similarity models


As already mentioned, the original and default similarity model available before Apache Lucene 6.0 was the TF-IDF model but in Lucene 6.0 it is changed to BM25, which we have already discussed in detail in The changed default text scoring in Lucene: BM25 section in Chapter 2, The Improved Query DSL.

Apart from BM25, other similarity models that we can use are:

  • TF-IDF (classic): This similarity model is based on TF-IDF model and used to be the default similarity model before Elasticsearch 5.0. In order to use this similarity in Elasticsearch, you need to use the classic name.

  • Divergence from randomness (DFR): This similarity model is based on the probabilistic model of the same name. In order to use this similarity in Elasticsearch, you need to use the DFR name. It is said that the divergence from the randomness similarity model performs well on text similar to natural language text.

  • Divergence from independence (DFI): This similarity model is based on the probabilistic...

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