# groonga - An open-source fulltext search engine and column store.

- 日本語

# 7.8. Tokenizers

## 7.8.1. Summary

Groonga has tokenizer module that tokenizes text. It is used when the following cases:

> - Indexing text

>   Tokenizer is used when indexing text.

> - Searching by query

>   Tokenizer is used when searching by query.

Tokenizer is an important module for full-text search. You can change trade-off between precision and recall by changing tokenizer.

Normally, TokenBigram is a suitable tokenizer. If you don't know much about tokenizer, it's recommended that you choose TokenBigram.

You can try a tokenizer by tokenize and table_tokenize. Here is an example to try TokenBigram tokenizer by tokenize:

Execution example:

    tokenize TokenBigram "Hello World"
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "He"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "el"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "ll"
    #     },
    #     {
    #       "position": 3,
    #       "force_prefix": false,
    #       "value": "lo"
    #     },
    #     {
    #       "position": 4,
    #       "force_prefix": false,
    #       "value": "o "
    #     },
    #     {
    #       "position": 5,
    #       "force_prefix": false,
    #       "value": " W"
    #     },
    #     {
    #       "position": 6,
    #       "force_prefix": false,
    #       "value": "Wo"
    #     },
    #     {
    #       "position": 7,
    #       "force_prefix": false,
    #       "value": "or"
    #     },
    #     {
    #       "position": 8,
    #       "force_prefix": false,
    #       "value": "rl"
    #     },
    #     {
    #       "position": 9,
    #       "force_prefix": false,
    #       "value": "ld"
    #     },
    #     {
    #       "position": 10,
    #       "force_prefix": false,
    #       "value": "d"
    #     }
    #   ]
    # ]

## 7.8.2. What is "tokenize"?

"tokenize" is the process that extracts zero or more tokens from a text. There are some "tokenize" methods.

For example, `Hello World` is tokenized to the following tokens by bigram tokenize method:

> - `He`
> - `el`
> - `ll`
> - `lo`
> - `o_` (`_` means a white-space)
> - `_W` (`_` means a white-space)
> - `Wo`
> - `or`
> - `rl`
> - `ld`

In the above example, 10 tokens are extracted from one text `Hello World`.

For example, `Hello World` is tokenized to the following tokens by white-space-separate tokenize method:

> - `Hello`
> - `World`

In the above example, 2 tokens are extracted from one text `Hello World`.

Token is used as search key. You can find indexed documents only by tokens that are extracted by used tokenize method. For example, you can find `Hello World` by `ll` with bigram tokenize method but you can't find `Hello World` by `ll` with white-space-separate tokenize method. Because white-space-separate tokenize method doesn't extract `ll` token. It just extracts `Hello` and `World` tokens.

In general, tokenize method that generates small tokens increases recall but decreases precision. Tokenize method that generates large tokens increases precision but decreases recall.

For example, we can find `Hello World` and `A or B` by `or` with bigram tokenize method. `Hello World` is a noise for people who wants to search "logical and". It means that precision is decreased. But recall is increased.

We can find only `A or B` by `or` with white-space-separate tokenize method. Because `World` is tokenized to one token `World` with white-space-separate tokenize method. It means that precision is increased for people who wants to search "logical and". But recall is decreased because `Hello World` that contains `or` isn't found.

## 7.8.3. Built-in tokenizsers

Here is a list of built-in tokenizers:

> - `TokenBigram`
> - `TokenBigramSplitSymbol`
> - `TokenBigramSplitSymbolAlpha`
> - `TokenBigramSplitSymbolAlphaDigit`
> - `TokenBigramIgnoreBlank`
> - `TokenBigramIgnoreBlankSplitSymbol`
> - `TokenBigramIgnoreBlankSplitAlpha`
> - `TokenBigramIgnoreBlankSplitAlphaDigit`
> - `TokenUnigram`
> - `TokenTrigram`
> - `TokenDelimit`
> - `TokenDelimitNull`
> - `TokenMecab`
> - `TokenRegexp`

### 7.8.3.1. `TokenBigram`

`TokenBigram` is a bigram based tokenizer. It's recommended to use this tokenizer for most cases.

Bigram tokenize method tokenizes a text to two adjacent characters tokens. For example, `Hello` is tokenized to the following tokens:

> - `He`
> - `el`
> - `ll`
> - `lo`

Bigram tokenize method is good for recall because you can find all texts by query consists of two or more characters.

In general, you can't find all texts by query consists of one character because one character token doesn't exist. But you can find all texts by query consists of one character in Groonga. Because Groonga find tokens that start with query by predictive search. For example, Groonga can find `ll` and `lo` tokens by `l` query.

Bigram tokenize method isn't good for precision because you can find texts that includes query in word. For example, you can find `world` by `or`. This is more sensitive for ASCII only languages rather than non-ASCII languages. `TokenBigram` has solution for this problem described in the below.

`TokenBigram` behavior is different when it's worked with any Normalizers.

If no normalizer is used, `TokenBigram` uses pure bigram (all tokens except the last token have two characters) tokenize method:

Execution example:

    tokenize TokenBigram "Hello World"
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "He"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "el"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "ll"
    #     },
    #     {
    #       "position": 3,
    #       "force_prefix": false,
    #       "value": "lo"
    #     },
    #     {
    #       "position": 4,
    #       "force_prefix": false,
    #       "value": "o "
    #     },
    #     {
    #       "position": 5,
    #       "force_prefix": false,
    #       "value": " W"
    #     },
    #     {
    #       "position": 6,
    #       "force_prefix": false,
    #       "value": "Wo"
    #     },
    #     {
    #       "position": 7,
    #       "force_prefix": false,
    #       "value": "or"
    #     },
    #     {
    #       "position": 8,
    #       "force_prefix": false,
    #       "value": "rl"
    #     },
    #     {
    #       "position": 9,
    #       "force_prefix": false,
    #       "value": "ld"
    #     },
    #     {
    #       "position": 10,
    #       "force_prefix": false,
    #       "value": "d"
    #     }
    #   ]
    # ]

If normalizer is used, `TokenBigram` uses white-space-separate like tokenize method for ASCII characters. `TokenBigram` uses bigram tokenize method for non-ASCII characters.

You may be confused with this combined behavior. But it's reasonable for most use cases such as English text (only ASCII characters) and Japanese text (ASCII and non-ASCII characters are mixed).

Most languages consists of only ASCII characters use white-space for word separator. White-space-separate tokenize method is suitable for the case.

Languages consists of non-ASCII characters don't use white-space for word separator. Bigram tokenize method is suitable for the case.

Mixed tokenize method is suitable for mixed language case.

If you want to use bigram tokenize method for ASCII character, see `TokenBigramSplitXXX` type tokenizers such as TokenBigramSplitSymbolAlpha.

Let's confirm `TokenBigram` behavior by example.

`TokenBigram` uses one or more white-spaces as token delimiter for ASCII characters:

Execution example:

    tokenize TokenBigram "Hello World" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "hello"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "world"
    #     }
    #   ]
    # ]

`TokenBigram` uses character type change as token delimiter for ASCII characters. Character type is one of them:

> - Alphabet
> - Digit
> - Symbol (such as `(`, `)` and `!`)
> - Hiragana
> - Katakana
> - Kanji
> - Others

The following example shows two token delimiters:

> - at between `100` (digits) and `cents` (alphabets)
> - at between `cents` (alphabets) and `!!!` (symbols)

Execution example:

    tokenize TokenBigram "100cents!!!" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "100"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "cents"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "!!!"
    #     }
    #   ]
    # ]

Here is an example that `TokenBigram` uses bigram tokenize method for non-ASCII characters.

Execution example:

    tokenize TokenBigram "日本語の勉強" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "日本"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "本語"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "語の"
    #     },
    #     {
    #       "position": 3,
    #       "force_prefix": false,
    #       "value": "の勉"
    #     },
    #     {
    #       "position": 4,
    #       "force_prefix": false,
    #       "value": "勉強"
    #     },
    #     {
    #       "position": 5,
    #       "force_prefix": false,
    #       "value": "強"
    #     }
    #   ]
    # ]

### 7.8.3.2. `TokenBigramSplitSymbol`

`TokenBigramSplitSymbol` is similar to TokenBigram. The difference between them is symbol handling. `TokenBigramSplitSymbol` tokenizes symbols by bigram tokenize method:

Execution example:

    tokenize TokenBigramSplitSymbol "100cents!!!" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "100"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "cents"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "!!"
    #     },
    #     {
    #       "position": 3,
    #       "force_prefix": false,
    #       "value": "!!"
    #     },
    #     {
    #       "position": 4,
    #       "force_prefix": false,
    #       "value": "!"
    #     }
    #   ]
    # ]

### 7.8.3.3. `TokenBigramSplitSymbolAlpha`

`TokenBigramSplitSymbolAlpha` is similar to TokenBigram. The difference between them is symbol and alphabet handling. `TokenBigramSplitSymbolAlpha` tokenizes symbols and alphabets by bigram tokenize method:

Execution example:

    tokenize TokenBigramSplitSymbolAlpha "100cents!!!" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "100"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "ce"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "en"
    #     },
    #     {
    #       "position": 3,
    #       "force_prefix": false,
    #       "value": "nt"
    #     },
    #     {
    #       "position": 4,
    #       "force_prefix": false,
    #       "value": "ts"
    #     },
    #     {
    #       "position": 5,
    #       "force_prefix": false,
    #       "value": "s!"
    #     },
    #     {
    #       "position": 6,
    #       "force_prefix": false,
    #       "value": "!!"
    #     },
    #     {
    #       "position": 7,
    #       "force_prefix": false,
    #       "value": "!!"
    #     },
    #     {
    #       "position": 8,
    #       "force_prefix": false,
    #       "value": "!"
    #     }
    #   ]
    # ]

### 7.8.3.4. `TokenBigramSplitSymbolAlphaDigit`

`TokenBigramSplitSymbolAlphaDigit` is similar to TokenBigram. The difference between them is symbol, alphabet and digit handling. `TokenBigramSplitSymbolAlphaDigit` tokenizes symbols, alphabets and digits by bigram tokenize method. It means that all characters are tokenized by bigram tokenize method:

Execution example:

    tokenize TokenBigramSplitSymbolAlphaDigit "100cents!!!" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "10"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "00"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "0c"
    #     },
    #     {
    #       "position": 3,
    #       "force_prefix": false,
    #       "value": "ce"
    #     },
    #     {
    #       "position": 4,
    #       "force_prefix": false,
    #       "value": "en"
    #     },
    #     {
    #       "position": 5,
    #       "force_prefix": false,
    #       "value": "nt"
    #     },
    #     {
    #       "position": 6,
    #       "force_prefix": false,
    #       "value": "ts"
    #     },
    #     {
    #       "position": 7,
    #       "force_prefix": false,
    #       "value": "s!"
    #     },
    #     {
    #       "position": 8,
    #       "force_prefix": false,
    #       "value": "!!"
    #     },
    #     {
    #       "position": 9,
    #       "force_prefix": false,
    #       "value": "!!"
    #     },
    #     {
    #       "position": 10,
    #       "force_prefix": false,
    #       "value": "!"
    #     }
    #   ]
    # ]

### 7.8.3.5. `TokenBigramIgnoreBlank`

`TokenBigramIgnoreBlank` is similar to TokenBigram. The difference between them is blank handling. `TokenBigramIgnoreBlank` ignores white-spaces in continuous symbols and non-ASCII characters.

You can find difference of them by `日 本 語 ! ! !` text because it has symbols and non-ASCII characters.

Here is a result by TokenBigram :

Execution example:

    tokenize TokenBigram "日 本 語 ! ! !" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "日"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "本"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "語"
    #     },
    #     {
    #       "position": 3,
    #       "force_prefix": false,
    #       "value": "!"
    #     },
    #     {
    #       "position": 4,
    #       "force_prefix": false,
    #       "value": "!"
    #     },
    #     {
    #       "position": 5,
    #       "force_prefix": false,
    #       "value": "!"
    #     }
    #   ]
    # ]

Here is a result by `TokenBigramIgnoreBlank`:

Execution example:

    tokenize TokenBigramIgnoreBlank "日 本 語 ! ! !" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "日本"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "本語"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "語"
    #     },
    #     {
    #       "position": 3,
    #       "force_prefix": false,
    #       "value": "!!!"
    #     }
    #   ]
    # ]

### 7.8.3.6. `TokenBigramIgnoreBlankSplitSymbol`

`TokenBigramIgnoreBlankSplitSymbol` is similar to TokenBigram. The differences between them are the followings:

> - Blank handling
> - Symbol handling

`TokenBigramIgnoreBlankSplitSymbol` ignores white-spaces in continuous symbols and non-ASCII characters.

`TokenBigramIgnoreBlankSplitSymbol` tokenizes symbols by bigram tokenize method.

You can find difference of them by `日 本 語 ! ! !` text because it has symbols and non-ASCII characters.

Here is a result by TokenBigram :

Execution example:

    tokenize TokenBigram "日 本 語 ! ! !" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "日"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "本"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "語"
    #     },
    #     {
    #       "position": 3,
    #       "force_prefix": false,
    #       "value": "!"
    #     },
    #     {
    #       "position": 4,
    #       "force_prefix": false,
    #       "value": "!"
    #     },
    #     {
    #       "position": 5,
    #       "force_prefix": false,
    #       "value": "!"
    #     }
    #   ]
    # ]

Here is a result by `TokenBigramIgnoreBlankSplitSymbol`:

Execution example:

    tokenize TokenBigramIgnoreBlankSplitSymbol "日 本 語 ! ! !" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "日本"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "本語"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "語!"
    #     },
    #     {
    #       "position": 3,
    #       "force_prefix": false,
    #       "value": "!!"
    #     },
    #     {
    #       "position": 4,
    #       "force_prefix": false,
    #       "value": "!!"
    #     },
    #     {
    #       "position": 5,
    #       "force_prefix": false,
    #       "value": "!"
    #     }
    #   ]
    # ]

### 7.8.3.7. `TokenBigramIgnoreBlankSplitSymbolAlpha`

`TokenBigramIgnoreBlankSplitSymbolAlpha` is similar to TokenBigram. The differences between them are the followings:

> - Blank handling
> - Symbol and alphabet handling

`TokenBigramIgnoreBlankSplitSymbolAlpha` ignores white-spaces in continuous symbols and non-ASCII characters.

`TokenBigramIgnoreBlankSplitSymbolAlpha` tokenizes symbols and alphabets by bigram tokenize method.

You can find difference of them by `Hello 日 本 語 ! ! !` text because it has symbols and non-ASCII characters with white spaces and alphabets.

Here is a result by TokenBigram :

Execution example:

    tokenize TokenBigram "Hello 日 本 語 ! ! !" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "hello"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "日"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "本"
    #     },
    #     {
    #       "position": 3,
    #       "force_prefix": false,
    #       "value": "語"
    #     },
    #     {
    #       "position": 4,
    #       "force_prefix": false,
    #       "value": "!"
    #     },
    #     {
    #       "position": 5,
    #       "force_prefix": false,
    #       "value": "!"
    #     },
    #     {
    #       "position": 6,
    #       "force_prefix": false,
    #       "value": "!"
    #     }
    #   ]
    # ]

Here is a result by `TokenBigramIgnoreBlankSplitSymbolAlpha`:

Execution example:

    tokenize TokenBigramIgnoreBlankSplitSymbolAlpha "Hello 日 本 語 ! ! !" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "he"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "el"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "ll"
    #     },
    #     {
    #       "position": 3,
    #       "force_prefix": false,
    #       "value": "lo"
    #     },
    #     {
    #       "position": 4,
    #       "force_prefix": false,
    #       "value": "o日"
    #     },
    #     {
    #       "position": 5,
    #       "force_prefix": false,
    #       "value": "日本"
    #     },
    #     {
    #       "position": 6,
    #       "force_prefix": false,
    #       "value": "本語"
    #     },
    #     {
    #       "position": 7,
    #       "force_prefix": false,
    #       "value": "語!"
    #     },
    #     {
    #       "position": 8,
    #       "force_prefix": false,
    #       "value": "!!"
    #     },
    #     {
    #       "position": 9,
    #       "force_prefix": false,
    #       "value": "!!"
    #     },
    #     {
    #       "position": 10,
    #       "force_prefix": false,
    #       "value": "!"
    #     }
    #   ]
    # ]

### 7.8.3.8. `TokenBigramIgnoreBlankSplitSymbolAlphaDigit`

`TokenBigramIgnoreBlankSplitSymbolAlphaDigit` is similar to TokenBigram. The differences between them are the followings:

> - Blank handling
> - Symbol, alphabet and digit handling

`TokenBigramIgnoreBlankSplitSymbolAlphaDigit` ignores white-spaces in continuous symbols and non-ASCII characters.

`TokenBigramIgnoreBlankSplitSymbolAlphaDigit` tokenizes symbols, alphabets and digits by bigram tokenize method. It means that all characters are tokenized by bigram tokenize method.

You can find difference of them by `Hello 日 本 語 ! ! ! 777` text because it has symbols and non-ASCII characters with white spaces, alphabets and digits.

Here is a result by TokenBigram :

Execution example:

    tokenize TokenBigram "Hello 日 本 語 ! ! ! 777" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "hello"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "日"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "本"
    #     },
    #     {
    #       "position": 3,
    #       "force_prefix": false,
    #       "value": "語"
    #     },
    #     {
    #       "position": 4,
    #       "force_prefix": false,
    #       "value": "!"
    #     },
    #     {
    #       "position": 5,
    #       "force_prefix": false,
    #       "value": "!"
    #     },
    #     {
    #       "position": 6,
    #       "force_prefix": false,
    #       "value": "!"
    #     },
    #     {
    #       "position": 7,
    #       "force_prefix": false,
    #       "value": "777"
    #     }
    #   ]
    # ]

Here is a result by `TokenBigramIgnoreBlankSplitSymbolAlphaDigit`:

Execution example:

    tokenize TokenBigramIgnoreBlankSplitSymbolAlphaDigit "Hello 日 本 語 ! ! ! 777" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "he"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "el"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "ll"
    #     },
    #     {
    #       "position": 3,
    #       "force_prefix": false,
    #       "value": "lo"
    #     },
    #     {
    #       "position": 4,
    #       "force_prefix": false,
    #       "value": "o日"
    #     },
    #     {
    #       "position": 5,
    #       "force_prefix": false,
    #       "value": "日本"
    #     },
    #     {
    #       "position": 6,
    #       "force_prefix": false,
    #       "value": "本語"
    #     },
    #     {
    #       "position": 7,
    #       "force_prefix": false,
    #       "value": "語!"
    #     },
    #     {
    #       "position": 8,
    #       "force_prefix": false,
    #       "value": "!!"
    #     },
    #     {
    #       "position": 9,
    #       "force_prefix": false,
    #       "value": "!!"
    #     },
    #     {
    #       "position": 10,
    #       "force_prefix": false,
    #       "value": "!7"
    #     },
    #     {
    #       "position": 11,
    #       "force_prefix": false,
    #       "value": "77"
    #     },
    #     {
    #       "position": 12,
    #       "force_prefix": false,
    #       "value": "77"
    #     },
    #     {
    #       "position": 13,
    #       "force_prefix": false,
    #       "value": "7"
    #     }
    #   ]
    # ]

### 7.8.3.9. `TokenUnigram`

`TokenUnigram` is similar to TokenBigram. The differences between them is token unit. TokenBigram uses 2 characters per token. `TokenUnigram` uses 1 character per token.

Execution example:

    tokenize TokenUnigram "100cents!!!" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "100"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "cents"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "!!!"
    #     }
    #   ]
    # ]

### 7.8.3.10. `TokenTrigram`

`TokenTrigram` is similar to TokenBigram. The differences between them is token unit. TokenBigram uses 2 characters per token. `TokenTrigram` uses 3 characters per token.

Execution example:

    tokenize TokenTrigram "10000cents!!!!!" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "10000"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "cents"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "!!!!!"
    #     }
    #   ]
    # ]

### 7.8.3.11. `TokenDelimit`

`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`.

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"
    #     }
    #   ]
    # ]

### 7.8.3.12. `TokenDelimitNull`

`TokenDelimitNull` is similar to TokenDelimit. The difference between them is separator character. TokenDelimit uses space character (`U+0020`) but `TokenDelimitNull` uses NUL character (`U+0000`).

`TokenDelimitNull` is also suitable for tag text.

Here is an example of `TokenDelimitNull`:

Execution example:

    tokenize TokenDelimitNull "Groonga\u0000full-text-search\u0000HTTP" NormalizerAuto
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "groongau0000full-text-searchu0000http"
    #     }
    #   ]
    # ]

### 7.8.3.13. `TokenMecab`

`TokenMecab` is a tokenizer based on MeCab part-of-speech and morphological analyzer.

MeCab doesn't depend on Japanese. You can use MeCab for other languages by creating dictionary for the languages. You can use NAIST Japanese Dictionary for Japanese.

`TokenMecab` is good for precision rather than recall. You can find `東京都` and `京都` texts by `京都` query with TokenBigram but `東京都` isn't expected. You can find only `京都` text by `京都` query with `TokenMecab`.

If you want to support neologisms, you need to keep updating your MeCab dictionary. It needs maintain cost. (TokenBigram doesn't require dictionary maintenance because TokenBigram doesn't use dictionary.) mecab-ipadic-NEologd : Neologism dictionary for MeCab may help you.

Here is an example of `TokenMeCab`. `東京都` is tokenized to `東京` and `都`. They don't include `京都`:

Execution example:

    tokenize TokenMecab "東京都"
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "東京"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "都"
    #     }
    #   ]
    # ]

### 7.8.3.14. `TokenRegexp`

New in version 5.0.1.

Caution

This tokenizer is experimental. Specification may be changed.

Caution

This tokenizer can be used only with UTF-8. You can't use this tokenizer with EUC-JP, Shift_JIS and so on.

`TokenRegexp` is a tokenizer for supporting regular expression search by index.

In general, regular expression search is evaluated as sequential search. But the following cases can be evaluated as index search:

> - Literal only case such as `hello`
> - The beginning of text and literal case such as `\A/home/alice`
> - The end of text and literal case such as `\.txt\z`

In most cases, index search is faster than sequential search.

`TokenRegexp` is based on bigram tokenize method. `TokenRegexp` adds the beginning of text mark (`U+FFEF`) at the begging of text and the end of text mark (`U+FFF0`) to the end of text when you index text:

Execution example:

    tokenize TokenRegexp "/home/alice/test.txt" NormalizerAuto --mode ADD
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     {
    #       "position": 0,
    #       "force_prefix": false,
    #       "value": "￯"
    #     },
    #     {
    #       "position": 1,
    #       "force_prefix": false,
    #       "value": "/h"
    #     },
    #     {
    #       "position": 2,
    #       "force_prefix": false,
    #       "value": "ho"
    #     },
    #     {
    #       "position": 3,
    #       "force_prefix": false,
    #       "value": "om"
    #     },
    #     {
    #       "position": 4,
    #       "force_prefix": false,
    #       "value": "me"
    #     },
    #     {
    #       "position": 5,
    #       "force_prefix": false,
    #       "value": "e/"
    #     },
    #     {
    #       "position": 6,
    #       "force_prefix": false,
    #       "value": "/a"
    #     },
    #     {
    #       "position": 7,
    #       "force_prefix": false,
    #       "value": "al"
    #     },
    #     {
    #       "position": 8,
    #       "force_prefix": false,
    #       "value": "li"
    #     },
    #     {
    #       "position": 9,
    #       "force_prefix": false,
    #       "value": "ic"
    #     },
    #     {
    #       "position": 10,
    #       "force_prefix": false,
    #       "value": "ce"
    #     },
    #     {
    #       "position": 11,
    #       "force_prefix": false,
    #       "value": "e/"
    #     },
    #     {
    #       "position": 12,
    #       "force_prefix": false,
    #       "value": "/t"
    #     },
    #     {
    #       "position": 13,
    #       "force_prefix": false,
    #       "value": "te"
    #     },
    #     {
    #       "position": 14,
    #       "force_prefix": false,
    #       "value": "es"
    #     },
    #     {
    #       "position": 15,
    #       "force_prefix": false,
    #       "value": "st"
    #     },
    #     {
    #       "position": 16,
    #       "force_prefix": false,
    #       "value": "t."
    #     },
    #     {
    #       "position": 17,
    #       "force_prefix": false,
    #       "value": ".t"
    #     },
    #     {
    #       "position": 18,
    #       "force_prefix": false,
    #       "value": "tx"
    #     },
    #     {
    #       "position": 19,
    #       "force_prefix": false,
    #       "value": "xt"
    #     },
    #     {
    #       "position": 20,
    #       "force_prefix": false,
    #       "value": "t"
    #     },
    #     {
    #       "position": 21,
    #       "force_prefix": false,
    #       "value": "￰"
    #     }
    #   ]
    # ]