anchor.diff:  
   The `phrase` suggester supports multiple smoothing models to balance
   weight between infrequent grams (grams (shingles) are not existing in
  -the index) and frequent grams (appear at least once in the index). 
  +the index) and frequent grams (appear at least once in the index).
   
   [horizontal]
  -`stupid_backoff`:: 
  +`stupid_backoff`::
       a simple backoff model that backs off to lower
       order n-gram models if the higher order count is `0` and discounts the
       lower order n-gram model by a constant factor. The default `discount` is
  -    `0.4`. Stupid Backoff is the default model. 
  +    `0.4`. Stupid Backoff is the default model.
   
   `laplace`::
       a smoothing model that uses an additive smoothing where a
       constant (typically `1.0` or smaller) is added to all counts to balance
  -    weights, The default `alpha` is `0.5`. 
  -
  -`linear_interpolation`:: 
  +    weights, The default `alpha` is `0.5`.
  +
  +`linear_interpolation`::
       a smoothing model that takes the weighted
       mean of the unigrams, bigrams and trigrams based on user supplied
       weights (lambdas). Linear Interpolation doesn't have any default values.
  
positive.code:     language model, the suggester will use this field to gain statistics to
      score corrections. This field is mandatory.
  
  `gram_size`:: 
      sets max size of the n-grams (shingles) in the `field`.
      If the field doesn't contain n-grams (shingles) this should be omitted
      or set to `1`. Note that Elasticsearch tries to detect the gram size
      based on the specified `field`. If the field uses a `shingle` filter the
      `gram_size` is set to the `max_shingle_size` if not explicitly set.
  
  `real_word_error_likelihood`:: 
      the likelihood of a term being a
      misspelled even if the term exists in the dictionary. The default is
      `0.95` corresponding to 5% of the real words are misspelled.
  
  
  `confidence`:: 
      The confidence level defines a factor applied to the
      input phrases score which is used as a threshold for other suggest
      candidates. Only candidates that score higher than the threshold will be
  
negative.code:     default is set to `1.0` which corresponds to that only corrections with
      at most 1 misspelled term are returned.  Note that setting this too high
      can negatively impact performance. Low values like `1` or `2` are recommended
      otherwise the time spend in suggest calls might exceed the time spend in
      query execution.
  
  `separator`::
      the separator that is used to separate terms in the
      bigram field. If not set the whitespace character is used as a
      separator.
  
  `size`::
      the number of candidates that are generated for each
      individual query term Low numbers like `3` or `5` typically produce good
      results. Raising this can bring up terms with higher edit distances. The
      default is `5`.
  
  `analyzer`::
      Sets the analyzer to analyse to suggest text with.
      Defaults to the search analyzer of the suggest field passed via `field`.
  
  `shard_size`::
      Sets the maximum number of suggested term to be
      retrieved from each individual shard. During the reduce phase, only the
      top N suggestions are returned based on the `size` option. Defaults to
      `5`.
  
  `text`::
      Sets the text / query to provide suggestions for.
  
  `highlight`::
      Sets up suggestion highlighting.  If not provided then
      no `highlighted` field is returned.  If provided must
      contain exactly `pre_tag` and `post_tag` which are
      wrapped around the changed tokens.  If multiple tokens
      in a row are changed the entire phrase of changed tokens
      is wrapped rather than each token.
  
  `collate`::
  