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Pattern Analyzer

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Pattern Analyzer

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The pattern analyzer uses a regular expression to split the text into terms. The regular expression should match the token separators not the tokens themselves. The regular expression defaults to \W+ (or all non-word characters).

Beware of Pathological Regular Expressions

The pattern analyzer uses Java Regular Expressions.

A badly written regular expression could run very slowly or even throw a StackOverflowError and cause the node it is running on to exit suddenly.

Definition

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It consists of:

Tokenizer

Pattern Tokenizer

Token Filters

Lower Case Token Filter
Stop Token Filter (disabled by default)

Example output

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POST _analyze
{
  "analyzer": "pattern",
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}

The above sentence would produce the following terms:

[ the, 2, quick, brown, foxes, jumped, over, the, lazy, dog, s, bone ]

Configuration

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The pattern analyzer accepts the following parameters:

`pattern`	A Java regular expression, defaults to `\W+`.
`flags`	Java regular expression flags. Flags should be pipe-separated, eg `"CASE_INSENSITIVE\|COMMENTS"`.
`lowercase`	Should terms be lowercased or not. Defaults to `true`.
`stopwords`	A pre-defined stop words list like `_english_` or an array containing a list of stop words. Defaults to `\_none_`.
`stopwords_path`	The path to a file containing stop words.

See the Stop Token Filter for more information about stop word configuration.

Example configuration

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In this example, we configure the pattern analyzer to split email addresses on non-word characters or on underscores (\W|_), and to lower-case the result:

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_email_analyzer": {
          "type":      "pattern",
          "pattern":   "\\W|_", 
          "lowercase": true
        }
      }
    }
  }
}

POST my_index/_analyze
{
  "analyzer": "my_email_analyzer",
  "text": "John_Smith@foo-bar.com"
}

The backslashes in the pattern need to be escaped when specifying the pattern as a JSON string.

The above example produces the following terms:

[ john, smith, foo, bar, com ]

CamelCase tokenizer

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The following more complicated example splits CamelCase text into tokens:

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "camel": {
          "type": "pattern",
          "pattern": "([^\\p{L}\\d]+)|(?<=\\D)(?=\\d)|(?<=\\d)(?=\\D)|(?<=[\\p{L}&&[^\\p{Lu}]])(?=\\p{Lu})|(?<=\\p{Lu})(?=\\p{Lu}[\\p{L}&&[^\\p{Lu}]])"
        }
      }
    }
  }
}

GET my_index/_analyze
{
  "analyzer": "camel",
  "text": "MooseX::FTPClass2_beta"
}

The above example produces the following terms:

[ moose, x, ftp, class, 2, beta ]

The regex above is easier to understand as:

  ([^\p{L}\d]+)                 # swallow non letters and numbers,
| (?<=\D)(?=\d)                 # or non-number followed by number,
| (?<=\d)(?=\D)                 # or number followed by non-number,
| (?<=[ \p{L} && [^\p{Lu}]])    # or lower case
  (?=\p{Lu})                    #   followed by upper case,
| (?<=\p{Lu})                   # or upper case
  (?=\p{Lu}                     #   followed by upper case
    [\p{L}&&[^\p{Lu}]]          #   then lower case
  )

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