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# 7.8.10. `TokenDelimit`

## 7.8.10.1. Summary

`TokenDelimit` extracts token by splitting one or more space characters (`U+0020`). For example, `Hello World` is tokenized to `Hello` and `World`.

`TokenDelimit` is suitable for tag text. You can extract `groonga` and `full-text-search` and `http` as tags from `groonga full-text-search http`.

## 7.8.10.2. Syntax

`TokenDelimit` has optional parameter.

No options(Extracts token by splitting one or more space characters (`U+0020`)):

    TokenDelimit

Specify delimiter:

    TokenDelimit("delimiter",  "delimiter1", delimiter", "delimiter2", ...)

Specify delimiter with regular expression:

    TokenDelimit("pattern", pattern)

The `delimiter` option and a `pattern` option are not use at the same time.

## 7.8.10.3. Usage

## 7.8.10.4. Simple usage

Here is an example of `TokenDelimit`:

Execution example:

    tokenize TokenDelimit "Groonga full-text-search HTTP" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "groonga"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "full-text-search"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "http"
    #     }
    #   ]
    # ]

`TokenDelimit` can also specify options. `TokenDelimit` has `delimiter` option and `pattern` option.

`delimiter` option can split token with a specified character.

For example, `Hello,World` is tokenized to `Hello` and `World` with `delimiter` option as below.

Execution example:

    tokenize 'TokenDelimit("delimiter", ",")' "Hello,World"
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "value": "Hello",
    #       "position": 0,
    #       "force_prefix": false,
    #       "force_prefix_search": false
    #     },
    #     {
    #       "value": "World",
    #       "position": 1,
    #       "force_prefix": false,
    #       "force_prefix_search": false
    #     }
    #   ]
    # ]

`pattern` option can split token with a regular expression. You can except needless space by `pattern` option.

For example, `This is a pen. This is an apple` is tokenized to `This is a pen` and `This is an apple` with `pattern` option as below.

Normally, when `This is a pen. This is an apple.` is splitted by `.`, needless spaces are included at the beginning of “This is an apple.”.

You can except the needless spaces by a `pattern` option as below example.

Execution example:

    tokenize 'TokenDelimit("pattern", "\\.\\s*")' "This is a pen. This is an apple."
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "value": "This is a pen.",
    #       "position": 0,
    #       "force_prefix": false,
    #       "force_prefix_search": false
    #     },
    #     {
    #       "value": "This is an apple.",
    #       "position": 1,
    #       "force_prefix": false,
    #       "force_prefix_search": false
    #     }
    #   ]
    # ]

## 7.8.10.5. Advanced usage

`delimiter` option can also specify multiple delimiters.

For example, `Hello, World` is tokenized to `Hello` and `World`. `,` and `` `` are delimiters in below example.

Execution example:

    tokenize 'TokenDelimit("delimiter", ",", "delimiter", " ")' "Hello, World"
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "value": "Hello",
    #       "position": 0,
    #       "force_prefix": false,
    #       "force_prefix_search": false
    #     },
    #     {
    #       "value": "World",
    #       "position": 1,
    #       "force_prefix": false,
    #       "force_prefix_search": false
    #     }
    #   ]
    # ]

You can extract token in complex conditions by `pattern` option.

For example, `これはペンですか！？リンゴですか？「リンゴです。」` is tokenize to `これはペンですか` and `リンゴですか`, `「リンゴです。」` with `delimiter` option as below.

Execution example:

    tokenize 'TokenDelimit("pattern", "([。！？]+(?![）」])|[\\r\\n]+)\\s*")' "これはペンですか！？リンゴですか？「リンゴです。」"
    # [
    #   [
    #     0,
    #     1545179416.22277,
    #     0.0002887248992919922
    #   ],
    #   [
    #     {
    #       "value": "これはペンですか",
    #       "position": 0,
    #       "force_prefix": false,
    #       "force_prefix_search": false
    #     },
    #     {
    #       "value": "リンゴですか",
    #       "position": 1,
    #       "force_prefix": false,
    #       "force_prefix_search": false
    #     },
    #     {
    #       "value": "「リンゴです。」",
    #       "position": 2,
    #       "force_prefix": false,
    #       "force_prefix_search": false
    #     }
    #   ]
    # ]

`\\s*` of the end of above regular expression match 0 or more spaces after a delimiter.

`[。！？]+` matches 1 or more `。` or `！`, `？`. For example, `[。！？]+` matches `！？` of `これはペンですか！？`.

`(?![）」])` is negative lookahead. `(?![）」])` matches if a character is not matched `）` or `」`. negative lookahead interprets in combination regular expression of just before.

Therefore it interprets `[。！？]+(?![）」])`.

`[。！？]+(?![）」])` matches if there are not `）` or `」` after `。` or `！`, `？`.

In other words, `[。！？]+(?![）」])` matches `。` of `これはペンですか。`. But `[。！？]+(?![）」])` doesn’t match `。` of `「リンゴです。」`. Because there is `」` after `。`.

`[\\r\\n]+` match 1 or more newline character.

In conclusion, `([。！？]+(?![）」])|[\\r\\n]+)\\s*` uses `。` and `！` and `？`, newline character as delimiter. However, `。` and `!`, `？` are not delimiters if there is `）` or `」` after `。` or `！`, `？`.

## 7.8.10.6. Parameters

### 7.8.10.6.1. Optional parameter

There are two optional parameters `delimiter` and `pattern`.

#### 7.8.10.6.1.1. `delimiter`

Split token with a specified one or more characters.

You can use one or more characters for a delimiter.

#### 7.8.10.6.1.2. `pattern`

Split token with a regular expression.

## 7.8.10.7. See also

- tokenize