A tokenizer accepts a string as input, processes the string to break it into individual words, or tokens (perhaps discarding some characters like punctuation), and emits a token stream as output.
What is interesting is the algorithm that is used to identify words. The
whitespace
tokenizer simply breaks on whitespace—spaces, tabs, line
feeds, and so forth—and assumes that contiguous nonwhitespace characters form a
single token. For instance:
GET /_analyze?tokenizer=whitespace
You're the 1st runner home!
This request would return the following terms:
You’re
, the
, 1st
, runner
, home!
The letter
tokenizer, on the other hand, breaks on any character that is
not a letter, and so would return the following terms: You
, re
, the
,
st
, runner
, home
.
The standard
tokenizer uses the Unicode Text Segmentation algorithm (as
defined in Unicode Standard Annex #29) to
find the boundaries between words, and emits everything in-between. Its
knowledge of Unicode allows it to successfully tokenize text containing a
mixture of languages.
Punctuation may or may not be considered part of a word, depending on where it appears:
GET /_analyze?tokenizer=standard
You're my 'favorite'.
In this example, the apostrophe in You’re
is treated as part of the
word, while the single quotes in 'favorite'
are not, resulting in the
following terms: You’re
, my
, favorite
.
Tip
|
The |
The standard
tokenizer is a reasonable starting point for tokenizing most
languages, especially Western languages. In fact, it forms the basis of most
of the language-specific analyzers like the english
, french
, and spanish
analyzers. Its support for Asian languages, however, is limited, and you should consider
using the icu_tokenizer
instead, which is available in the ICU plug-in.