anchor.diff:  Type name: `classic`
   
   [float]
  -[[drf]]
  +[[dfr]]
   ==== DFR similarity
   
   Similarity that implements the
  -http://lucene.apache.org/core/5_2_1/core/org/apache/lucene/search/similarities/DFRSimilarity.html[divergence
  +{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: `be`, `d`, `g`, `if`, `in`, `ine` and `p`.
  +    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: `no`, `b` and `l`.
  +    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: `no`, `h1`, `h2`, `h3` and `z`.
  +    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.
   
  
positive.code: This similarity has the following options:
  
  [horizontal]
  `independence_measure`:: Possible values `standardized`, `saturated`, `chisquared`.
  
  Type name: `DFI`
  
  
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]
  