7.8.2. TokenBigram#
7.8.2.1. Summary#
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.
7.8.2.2. Syntax#
TokenBigram hasn’t parameter:
TokenBigram
7.8.2.3. Usage#
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
# ],
# [
# {
# "value": "He",
# "position": 0,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "el",
# "position": 1,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "ll",
# "position": 2,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "lo",
# "position": 3,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "o ",
# "position": 4,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": " W",
# "position": 5,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "Wo",
# "position": 6,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "or",
# "position": 7,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "rl",
# "position": 8,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "ld",
# "position": 9,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "d",
# "position": 10,
# "force_prefix": false,
# "force_prefix_search": false
# }
# ]
# ]
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
# ],
# [
# {
# "value": "hello",
# "position": 0,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "world",
# "position": 1,
# "force_prefix": false,
# "force_prefix_search": false
# }
# ]
# ]
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) andcents(alphabets)at between
cents(alphabets) and!!!(symbols)
Execution example:
tokenize TokenBigram "100cents!!!" NormalizerAuto
# [
# [
# 0,
# 1337566253.89858,
# 0.000355720520019531
# ],
# [
# {
# "value": "100",
# "position": 0,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "cents",
# "position": 1,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "!!!",
# "position": 2,
# "force_prefix": false,
# "force_prefix_search": false
# }
# ]
# ]
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
# ],
# [
# {
# "value": "日本",
# "position": 0,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "本語",
# "position": 1,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "語の",
# "position": 2,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "の勉",
# "position": 3,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "勉強",
# "position": 4,
# "force_prefix": false,
# "force_prefix_search": false
# },
# {
# "value": "強",
# "position": 5,
# "force_prefix": false,
# "force_prefix_search": false
# }
# ]
# ]