anchor.diff:  [[lm_dirichlet]]
   ==== LM Dirichlet similarity.
   
  -http://lucene.apache.org/core/5_2_1/core/org/apache/lucene/search/similarities/LMDirichletSimilarity.html[LM
  +{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.
   
  -http://lucene.apache.org/core/5_2_1/core/org/apache/lucene/search/similarities/LMJelinekMercerSimilarity.html[LM
  +{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]
  
positive.code: [[ib]]
  ==== IB similarity.
  
  http://lucene.apache.org/core/5_2_1/core/org/apache/lucene/search/similarities/IBSimilarity.html[Information
  based model] . The algorithm is based on the concept that the information content in any symbolic 'distribution'
  sequence is primarily determined by the repetitive usage of its basic elements.
  For written texts this challenge would correspond to comparing the writing styles of different authors.
  This similarity has the following options:
  
  [horizontal]
  `distribution`::  Possible values: `ll` and `spl`.
  `lambda`::        Possible values: `df` and `ttf`.
  `normalization`:: Same as in `DFR` similarity.
  
  Type name: `IB`
  
negative.code: Type name: `classic`
  
  [float]
  [[dfr]]
  ==== DFR similarity
  
  Similarity that implements the
  {lucene-core-javadoc}/org/apache/lucene/search/similarities/DFRSimilarity.html[divergence
  from randomness] framework. This similarity has the following options:
  
  [horizontal]
  `basic_model`::
      Possible values: {lucene-core-javadoc}/org/apache/lucene/search/similarities/BasicModelG.html[`be`],
      {lucene-core-javadoc}/org/apache/lucene/search/similarities/BasicModelD.html[`d`],
      {lucene-core-javadoc}/org/apache/lucene/search/similarities/BasicModelG.html[`g`],
      {lucene-core-javadoc}/org/apache/lucene/search/similarities/BasicModelIF.html[`if`],
      {lucene-core-javadoc}/org/apache/lucene/search/similarities/BasicModelIn.html[`in`],
      {lucene-core-javadoc}/org/apache/lucene/search/similarities/BasicModelIne.html[`ine`] and
      {lucene-core-javadoc}/org/apache/lucene/search/similarities/BasicModelP.html[`p`].
  
  `be`, `d` and `p` should be avoided in practice as they might return scores that
  are equal to 0 or infinite with terms that do not meet the expected random
  distribution.
  
  `after_effect`::
      Possible values: {lucene-core-javadoc}/org/apache/lucene/search/similarities/AfterEffect.NoAfterEffect.html[`no`],
      {lucene-core-javadoc}/org/apache/lucene/search/similarities/AfterEffectB.html[`b`] and
      {lucene-core-javadoc}/org/apache/lucene/search/similarities/AfterEffectL.html[`l`].
  
  `normalization`::
      Possible values: {lucene-core-javadoc}/org/apache/lucene/search/similarities/Normalization.NoNormalization.html[`no`],
      {lucene-core-javadoc}/org/apache/lucene/search/similarities/NormalizationH1.html[`h1`],
      {lucene-core-javadoc}/org/apache/lucene/search/similarities/NormalizationH2.html[`h2`],
      {lucene-core-javadoc}/org/apache/lucene/search/similarities/NormalizationH1.html[`h3`] and
      {lucene-core-javadoc}/org/apache/lucene/search/similarities/NormalizationZ.html[`z`].
  
  All options but the first option need a normalization value.
  
  