anchor.diff:  This similarity has the following options:
   
   [horizontal]
  -`independence_measure`:: Possible values `standardized`, `saturated`, `chisquared`.
  +`independence_measure`:: Possible values
  +    {lucene-core-javadoc}/org/apache/lucene/search/similarities/IndependenceStandardized.html[`standardized`],
  +    {lucene-core-javadoc}/org/apache/lucene/search/similarities/IndependenceSaturated.html[`saturated`],
  +    {lucene-core-javadoc}/org/apache/lucene/search/similarities/IndependenceChiSquared.html[`chisquared`].
  +
  +When using this similarity, it is highly recommended to remove stop words to get
  +good relevance. Also beware that terms whose frequency is less than the expected
  +frequency will get a score equal to 0.
   
   Type name: `DFI`
   
  
positive.code: Type name: `classic`
  
  [float]
  [[drf]]
  ==== DFR similarity
  
  Similarity that implements the
  http://lucene.apache.org/core/5_2_1/core/org/apache/lucene/search/similarities/DFRSimilarity.html[divergence
  from randomness] framework. This similarity has the following options:
  
  [horizontal]
  `basic_model`::
      Possible values: `be`, `d`, `g`, `if`, `in`, `ine` and `p`.
  
  `after_effect`::
      Possible values: `no`, `b` and `l`.
  
  `normalization`::
      Possible values: `no`, `h1`, `h2`, `h3` and `z`.
  
  All options but the first option need a normalization value.
  
  
negative.code: [[lm_dirichlet]]
  ==== LM Dirichlet similarity.
  
  {lucene-core-javadoc}/org/apache/lucene/search/similarities/LMDirichletSimilarity.html[LM
  Dirichlet similarity] . This similarity has the following options:
  
  [horizontal]
  `mu`::  Default to `2000`.
  
  The scoring formula in the paper assigns negative scores to terms that have
  fewer occurrences than predicted by the language model, which is illegal to
  Lucene, so such terms get a score of 0.
  
  Type name: `LMDirichlet`
  
  [float]
  [[lm_jelinek_mercer]]
  ==== LM Jelinek Mercer similarity.
  
  {lucene-core-javadoc}/core/org/apache/lucene/search/similarities/LMJelinekMercerSimilarity.html[LM
  Jelinek Mercer similarity] . The algorithm attempts to capture important patterns in the text, while leaving out noise. This similarity has the following options:
  
  [horizontal]
  