[[analysis-word-delimiter-tokenfilter]]
=== Word delimiter token filter
++++
<titleabbrev>Word delimiter</titleabbrev>
++++

[WARNING]
====
We recommend using the
<<analysis-word-delimiter-graph-tokenfilter,`word_delimiter_graph`>> instead of
the `word_delimiter` filter.

The `word_delimiter` filter can produce invalid token graphs. See
<<analysis-word-delimiter-graph-differences>>.

The `word_delimiter` filter also uses Lucene's
{lucene-analysis-docs}/miscellaneous/WordDelimiterFilter.html[WordDelimiterFilter],
which is marked as deprecated. 
====

Splits tokens at non-alphanumeric characters. The `word_delimiter` 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`

[TIP]
====
The `word_delimiter` 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` filter with the
<<analysis-keyword-tokenizer,`keyword`>> tokenizer.

Avoid using the `word_delimiter` 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
<<analysis-synonym-graph-tokenfilter,`synonym_graph`>> filter instead.
====

[[analysis-word-delimiter-tokenfilter-analyze-ex]]
==== Example

The following <<indices-analyze,analyze API>> request uses the
`word_delimiter` filter to split `Neil's-Super-Duper-XL500--42+AutoCoder`
into normalized tokens using the filter's default rules:

[source,console]
----
GET /_analyze
{
  "tokenizer": "keyword",
  "filter": [ "word_delimiter" ],
  "text": "Neil's-Super-Duper-XL500--42+AutoCoder"
}
----

The filter produces the following tokens:

[source,txt]
----
[ Neil, Super, Duper, XL, 500, 42, Auto, Coder ]
----

////
[source,console-result]
----
{
  "tokens": [
    {
      "token": "Neil",
      "start_offset": 0,
      "end_offset": 4,
      "type": "word",
      "position": 0
    },
    {
      "token": "Super",
      "start_offset": 7,
      "end_offset": 12,
      "type": "word",
      "position": 1
    },
    {
      "token": "Duper",
      "start_offset": 13,
      "end_offset": 18,
      "type": "word",
      "position": 2
    },
    {
      "token": "XL",
      "start_offset": 19,
      "end_offset": 21,
      "type": "word",
      "position": 3
    },
    {
      "token": "500",
      "start_offset": 21,
      "end_offset": 24,
      "type": "word",
      "position": 4
    },
    {
      "token": "42",
      "start_offset": 26,
      "end_offset": 28,
      "type": "word",
      "position": 5
    },
    {
      "token": "Auto",
      "start_offset": 29,
      "end_offset": 33,
      "type": "word",
      "position": 6
    },
    {
      "token": "Coder",
      "start_offset": 33,
      "end_offset": 38,
      "type": "word",
      "position": 7
    }
  ]
}
----
////

[analysis-word-delimiter-tokenfilter-analyzer-ex]]
==== Add to an analyzer

The following <<indices-create-index,create index API>> request uses the
`word_delimiter` filter to configure a new
<<analysis-custom-analyzer,custom analyzer>>.

[source,console]
----
PUT /my-index-000001
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "tokenizer": "keyword",
          "filter": [ "word_delimiter" ]
        }
      }
    }
  }
}
----

[WARNING]
====
Avoid using the `word_delimiter` filter with tokenizers that remove punctuation,
such as the <<analysis-standard-tokenizer,`standard`>> tokenizer. This could
prevent the `word_delimiter` 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
<<analysis-keyword-tokenizer,`keyword`>> or
<<analysis-whitespace-tokenizer,`whitespace`>> tokenizer instead.
====

[[word-delimiter-tokenfilter-configure-parms]]
==== Configurable parameters

`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` -> [ `super`, **`superduperxl500`**, `duper`, `xl`, `500`
]. Defaults to `false`.

[WARNING]
====
When used for search analysis, catenated tokens can cause problems for the
<<query-dsl-match-query-phrase,`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` ->
[ `01`, **`010203`**, `02`, `03` ]. Defaults to `false`.

[WARNING]
====
When used for search analysis, catenated tokens can cause problems for the
<<query-dsl-match-query-phrase,`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`
-> [ `super`, **`superduperxl`**, `duper`, `xl` ]. Defaults to `false`.

[WARNING]
====
When used for search analysis, catenated tokens can cause problems for the
<<query-dsl-match-query-phrase,`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`.

`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`.

`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:

[source,txt]
----
# 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.
--

[[analysis-word-delimiter-tokenfilter-customize]]
==== Customize

To customize the `word_delimiter` 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`
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.

[source,console]
----
PUT /my-index-000001
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "tokenizer": "keyword",
          "filter": [ "my_custom_word_delimiter_filter" ]
        }
      },
      "filter": {
        "my_custom_word_delimiter_filter": {
          "type": "word_delimiter",
          "type_table": [ "- => ALPHA" ],
          "split_on_case_change": false,
          "split_on_numerics": false,
          "stem_english_possessive": true
        }
      }
    }
  }
}
----
