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« Word delimiter token filter Character filters reference »
Elastic Docs ›Elasticsearch Guide [master] ›Text analysis ›Token filter reference

Word delimiter graph token filter

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Word delimiter graph token filter

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Splits tokens at non-alphanumeric characters. The word_delimiter_graph filter also performs optional token normalization based on a set of rules. By default, the filter uses the following rules:

  • Split tokens at non-alphanumeric characters. The filter uses these characters as delimiters. For example: Super-Duper → Super, Duper
  • Remove leading or trailing delimiters from each token. For example: XL---42+'Autocoder' → XL, 42, Autocoder
  • Split tokens at letter case transitions. For example: PowerShot → Power, Shot
  • Split tokens at letter-number transitions. For example: XL500 → XL, 500
  • Remove the English possessive ('s) from the end of each token. For example: Neil's → Neil

The word_delimiter_graph filter uses Lucene’s WordDelimiterGraphFilter.

The word_delimiter_graph filter was designed to remove punctuation from complex identifiers, such as product IDs or part numbers. For these use cases, we recommend using the word_delimiter_graph filter with the keyword tokenizer.

Avoid using the word_delimiter_graph filter to split hyphenated words, such as wi-fi. Because users often search for these words both with and without hyphens, we recommend using the synonym_graph filter instead.

Example

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The following analyze API request uses the word_delimiter_graph filter to split Neil's-Super-Duper-XL500--42+AutoCoder into normalized tokens using the filter’s default rules:

resp = client.indices.analyze(
    tokenizer="keyword",
    filter=[
        "word_delimiter_graph"
    ],
    text="Neil's-Super-Duper-XL500--42+AutoCoder",
)
print(resp)
response = client.indices.analyze(
  body: {
    tokenizer: 'keyword',
    filter: [
      'word_delimiter_graph'
    ],
    text: "Neil's-Super-Duper-XL500--42+AutoCoder"
  }
)
puts response
const response = await client.indices.analyze({
  tokenizer: "keyword",
  filter: ["word_delimiter_graph"],
  text: "Neil's-Super-Duper-XL500--42+AutoCoder",
});
console.log(response);
GET /_analyze
{
  "tokenizer": "keyword",
  "filter": [ "word_delimiter_graph" ],
  "text": "Neil's-Super-Duper-XL500--42+AutoCoder"
}

The filter produces the following tokens:

[ Neil, Super, Duper, XL, 500, 42, Auto, Coder ]

Add to an analyzer

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The following create index API request uses the word_delimiter_graph filter to configure a new custom analyzer.

resp = client.indices.create(
    index="my-index-000001",
    settings={
        "analysis": {
            "analyzer": {
                "my_analyzer": {
                    "tokenizer": "keyword",
                    "filter": [
                        "word_delimiter_graph"
                    ]
                }
            }
        }
    },
)
print(resp)
response = client.indices.create(
  index: 'my-index-000001',
  body: {
    settings: {
      analysis: {
        analyzer: {
          my_analyzer: {
            tokenizer: 'keyword',
            filter: [
              'word_delimiter_graph'
            ]
          }
        }
      }
    }
  }
)
puts response
const response = await client.indices.create({
  index: "my-index-000001",
  settings: {
    analysis: {
      analyzer: {
        my_analyzer: {
          tokenizer: "keyword",
          filter: ["word_delimiter_graph"],
        },
      },
    },
  },
});
console.log(response);
PUT /my-index-000001
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "tokenizer": "keyword",
          "filter": [ "word_delimiter_graph" ]
        }
      }
    }
  }
}

Avoid using the word_delimiter_graph filter with tokenizers that remove punctuation, such as the standard tokenizer. This could prevent the word_delimiter_graph filter from splitting tokens correctly. It can also interfere with the filter’s configurable parameters, such as catenate_all or preserve_original. We recommend using the keyword or whitespace tokenizer instead.

Configurable parameters

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adjust_offsets

(Optional, Boolean) If true, the filter adjusts the offsets of split or catenated tokens to better reflect their actual position in the token stream. Defaults to true.

Set adjust_offsets to false if your analyzer uses filters, such as the trim filter, that change the length of tokens without changing their offsets. Otherwise, the word_delimiter_graph filter could produce tokens with illegal offsets.

catenate_all

(Optional, Boolean) If true, the filter produces catenated tokens for chains of alphanumeric characters separated by non-alphabetic delimiters. For example: super-duper-xl-500 → [ superduperxl500, super, duper, xl, 500 ]. Defaults to false.

Setting this parameter to true produces multi-position tokens, which are not supported by indexing.

If this parameter is true, avoid using this filter in an index analyzer or use the flatten_graph filter after this filter to make the token stream suitable for indexing.

When used for search analysis, catenated tokens can cause problems for the match_phrase query and other queries that rely on token position for matching. Avoid setting this parameter to true if you plan to use these queries.

catenate_numbers

(Optional, Boolean) If true, the filter produces catenated tokens for chains of numeric characters separated by non-alphabetic delimiters. For example: 01-02-03 → [ 010203, 01, 02, 03 ]. Defaults to false.

Setting this parameter to true produces multi-position tokens, which are not supported by indexing.

If this parameter is true, avoid using this filter in an index analyzer or use the flatten_graph filter after this filter to make the token stream suitable for indexing.

When used for search analysis, catenated tokens can cause problems for the match_phrase query and other queries that rely on token position for matching. Avoid setting this parameter to true if you plan to use these queries.

catenate_words

(Optional, Boolean) If true, the filter produces catenated tokens for chains of alphabetical characters separated by non-alphabetic delimiters. For example: super-duper-xl → [ superduperxl, super, duper, xl ]. Defaults to false.

Setting this parameter to true produces multi-position tokens, which are not supported by indexing.

If this parameter is true, avoid using this filter in an index analyzer or use the flatten_graph filter after this filter to make the token stream suitable for indexing.

When used for search analysis, catenated tokens can cause problems for the match_phrase query and other queries that rely on token position for matching. Avoid setting this parameter to true if you plan to use these queries.

generate_number_parts
(Optional, Boolean) If true, the filter includes tokens consisting of only numeric characters in the output. If false, the filter excludes these tokens from the output. Defaults to true.
generate_word_parts
(Optional, Boolean) If true, the filter includes tokens consisting of only alphabetical characters in the output. If false, the filter excludes these tokens from the output. Defaults to true.
ignore_keywords
(Optional, Boolean) If true, the filter skips tokens with a keyword attribute of true. Defaults to false.
preserve_original

(Optional, Boolean) If true, the filter includes the original version of any split tokens in the output. This original version includes non-alphanumeric delimiters. For example: super-duper-xl-500 → [ super-duper-xl-500, super, duper, xl, 500 ]. Defaults to false.

Setting this parameter to true produces multi-position tokens, which are not supported by indexing.

If this parameter is true, avoid using this filter in an index analyzer or use the flatten_graph filter after this filter to make the token stream suitable for indexing.

protected_words
(Optional, array of strings) Array of tokens the filter won’t split.
protected_words_path

(Optional, string) Path to a file that contains a list of tokens the filter won’t split.

This path must be absolute or relative to the config location, and the file must be UTF-8 encoded. Each token in the file must be separated by a line break.

split_on_case_change
(Optional, Boolean) If true, the filter splits tokens at letter case transitions. For example: camelCase → [ camel, Case ]. Defaults to true.
split_on_numerics
(Optional, Boolean) If true, the filter splits tokens at letter-number transitions. For example: j2se → [ j, 2, se ]. Defaults to true.
stem_english_possessive
(Optional, Boolean) If true, the filter removes the English possessive ('s) from the end of each token. For example: O'Neil's → [ O, Neil ]. Defaults to true.
type_table

(Optional, array of strings) Array of custom type mappings for characters. This allows you to map non-alphanumeric characters as numeric or alphanumeric to avoid splitting on those characters.

For example, the following array maps the plus (+) and hyphen (-) characters as alphanumeric, which means they won’t be treated as delimiters:

[ "+ => ALPHA", "- => ALPHA" ]

Supported types include:

  • ALPHA (Alphabetical)
  • ALPHANUM (Alphanumeric)
  • DIGIT (Numeric)
  • LOWER (Lowercase alphabetical)
  • SUBWORD_DELIM (Non-alphanumeric delimiter)
  • UPPER (Uppercase alphabetical)
type_table_path

(Optional, string) Path to a file that contains custom type mappings for characters. This allows you to map non-alphanumeric characters as numeric or alphanumeric to avoid splitting on those characters.

For example, the contents of this file may contain the following:

# Map the $, %, '.', and ',' characters to DIGIT
# This might be useful for financial data.
$ => DIGIT
% => DIGIT
. => DIGIT
\\u002C => DIGIT

# in some cases you might not want to split on ZWJ
# this also tests the case where we need a bigger byte[]
# see https://en.wikipedia.org/wiki/Zero-width_joiner
\\u200D => ALPHANUM

Supported types include:

  • ALPHA (Alphabetical)
  • ALPHANUM (Alphanumeric)
  • DIGIT (Numeric)
  • LOWER (Lowercase alphabetical)
  • SUBWORD_DELIM (Non-alphanumeric delimiter)
  • UPPER (Uppercase alphabetical)

This file path must be absolute or relative to the config location, and the file must be UTF-8 encoded. Each mapping in the file must be separated by a line break.

Customize

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To customize the word_delimiter_graph filter, duplicate it to create the basis for a new custom token filter. You can modify the filter using its configurable parameters.

For example, the following request creates a word_delimiter_graph filter that uses the following rules:

  • Split tokens at non-alphanumeric characters, except the hyphen (-) character.
  • Remove leading or trailing delimiters from each token.
  • Do not split tokens at letter case transitions.
  • Do not split tokens at letter-number transitions.
  • Remove the English possessive ('s) from the end of each token. 
resp = client.indices.create(
    index="my-index-000001",
    settings={
        "analysis": {
            "analyzer": {
                "my_analyzer": {
                    "tokenizer": "keyword",
                    "filter": [
                        "my_custom_word_delimiter_graph_filter"
                    ]
                }
            },
            "filter": {
                "my_custom_word_delimiter_graph_filter": {
                    "type": "word_delimiter_graph",
                    "type_table": [
                        "- => ALPHA"
                    ],
                    "split_on_case_change": False,
                    "split_on_numerics": False,
                    "stem_english_possessive": True
                }
            }
        }
    },
)
print(resp)
response = client.indices.create(
  index: 'my-index-000001',
  body: {
    settings: {
      analysis: {
        analyzer: {
          my_analyzer: {
            tokenizer: 'keyword',
            filter: [
              'my_custom_word_delimiter_graph_filter'
            ]
          }
        },
        filter: {
          my_custom_word_delimiter_graph_filter: {
            type: 'word_delimiter_graph',
            type_table: [
              '- => ALPHA'
            ],
            split_on_case_change: false,
            split_on_numerics: false,
            stem_english_possessive: true
          }
        }
      }
    }
  }
)
puts response
const response = await client.indices.create({
  index: "my-index-000001",
  settings: {
    analysis: {
      analyzer: {
        my_analyzer: {
          tokenizer: "keyword",
          filter: ["my_custom_word_delimiter_graph_filter"],
        },
      },
      filter: {
        my_custom_word_delimiter_graph_filter: {
          type: "word_delimiter_graph",
          type_table: ["- => ALPHA"],
          split_on_case_change: false,
          split_on_numerics: false,
          stem_english_possessive: true,
        },
      },
    },
  },
});
console.log(response);
PUT /my-index-000001
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "tokenizer": "keyword",
          "filter": [ "my_custom_word_delimiter_graph_filter" ]
        }
      },
      "filter": {
        "my_custom_word_delimiter_graph_filter": {
          "type": "word_delimiter_graph",
          "type_table": [ "- => ALPHA" ],
          "split_on_case_change": false,
          "split_on_numerics": false,
          "stem_english_possessive": true
        }
      }
    }
  }
}

Differences between word_delimiter_graph and word_delimiter

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Both the word_delimiter_graph and word_delimiter filters produce tokens that span multiple positions when any of the following parameters are true:

However, only the word_delimiter_graph filter assigns multi-position tokens a positionLength attribute, which indicates the number of positions a token spans. This ensures the word_delimiter_graph filter always produces valid token graphs.

The word_delimiter filter does not assign multi-position tokens a positionLength attribute. This means it produces invalid graphs for streams including these tokens.

While indexing does not support token graphs containing multi-position tokens, queries, such as the match_phrase query, can use these graphs to generate multiple sub-queries from a single query string.

To see how token graphs produced by the word_delimiter and word_delimiter_graph filters differ, check out the following example.

Example

Basic token graph

Both the word_delimiter and word_delimiter_graph produce the following token graph for PowerShot2000 when the following parameters are false:

This graph does not contain multi-position tokens. All tokens span only one position.

token graph basic

word_delimiter_graph graph with a multi-position token

The word_delimiter_graph filter produces the following token graph for PowerShot2000 when catenate_words is true.

This graph correctly indicates the catenated PowerShot token spans two positions.

token graph wdg

word_delimiter graph with a multi-position token

When catenate_words is true, the word_delimiter filter produces the following token graph for PowerShot2000.

Note that the catenated PowerShot token should span two positions but only spans one in the token graph, making it invalid.

token graph wd
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