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Fingerprint Analyzer

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IMPORTANT: This documentation is no longer updated. Refer to Elastic's version policy and the latest documentation.

Fingerprint Analyzer

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The fingerprint analyzer implements a fingerprinting algorithm which is used by the OpenRefine project to assist in clustering.

Input text is lowercased, normalized to remove extended characters, sorted, deduplicated and concatenated into a single token. If a stopword list is configured, stop words will also be removed.

Definition

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It consists of:

Tokenizer

Standard Tokenizer

Token Filters (in order)

Example output

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POST _analyze
{
  "analyzer": "fingerprint",
  "text": "Yes yes, Gödel said this sentence is consistent and."
}

The above sentence would produce the following single term:

[ and consistent godel is said sentence this yes ]

Configuration

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The fingerprint analyzer accepts the following parameters:

`separator`	The character to use to concate the terms. Defaults to a space.
`max_output_size`	The maximum token size to emit. Defaults to `255`. Tokens larger than this size will be discarded.
`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 fingerprint analyzer to use the pre-defined list of English stop words:

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_fingerprint_analyzer": {
          "type": "fingerprint",
          "stopwords": "_english_"
        }
      }
    }
  }
}

POST my_index/_analyze
{
  "analyzer": "my_fingerprint_analyzer",
  "text": "Yes yes, Gödel said this sentence is consistent and."
}

The above example produces the following term:

[ consistent godel said sentence yes ]

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