|  | @@ -44,6 +44,54 @@ The above sentence would produce the following term:
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				|  |  |  [ New York ]
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				|  |  |  ---------------------------
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				|  |  |  
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				|  |  | +[discrete]
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				|  |  | +[[analysis-keyword-tokenizer-token-filters]]
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				|  |  | +=== Combine with token filters
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				|  |  | +You can combine the `keyword` tokenizer with token filters to normalise
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				|  |  | +structured data, such as product IDs or email addresses.
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				|  |  | +
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				|  |  | +For example, the following <<indices-analyze,analyze API>> request uses
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				|  |  | +`keyword` tokenizer and <<analysis-lowercase-tokenfilter,`lowercase`>> filter to
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				|  |  | +convert an email address to lowercase.
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				|  |  | +
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				|  |  | +[source,console]
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				|  |  | +---------------------------
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				|  |  | +POST _analyze
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				|  |  | +{
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				|  |  | +  "tokenizer": "keyword",
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				|  |  | +  "filter": [ "lowercase" ],
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				|  |  | +  "text": "john.SMITH@example.COM"
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				|  |  | +}
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				|  |  | +---------------------------
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				|  |  | +
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				|  |  | +/////////////////////
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				|  |  | +
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				|  |  | +[source,console-result]
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				|  |  | +----------------------------
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				|  |  | +{
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				|  |  | +  "tokens": [
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				|  |  | +    {
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				|  |  | +      "token": "john.smith@example.com",
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				|  |  | +      "start_offset": 0,
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				|  |  | +      "end_offset": 22,
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				|  |  | +      "type": "word",
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				|  |  | +      "position": 0
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				|  |  | +    }
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				|  |  | +  ]
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				|  |  | +}
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				|  |  | +----------------------------
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				|  |  | +
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				|  |  | +/////////////////////
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				|  |  | +
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				|  |  | +
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				|  |  | +The request produces the following token:
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				|  |  | +
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				|  |  | +[source,text]
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				|  |  | +---------------------------
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				|  |  | +[ john.smith@example.com ]
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				|  |  | +---------------------------
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				|  |  | +
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				|  |  | +
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				|  |  |  [float]
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				|  |  |  === Configuration
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				|  |  |  
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