[[standard-tokenizer]] === standard Tokenizer

A tokenizer accepts a string as input, processes((("words", "identifying", "using standard tokenizer")))((("standard tokenizer")))((("tokenizers"))) 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 ((("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:

[source,js]

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 ((("letter tokenizer")))return the following terms: You, re, the, st, runner, home.

The standard tokenizer((("Unicode Text Segmentation algorithm"))) uses the Unicode Text Segmentation algorithm (as defined in http://unicode.org/reports/tr29/[Unicode Standard Annex #29]) to find the boundaries between words,((("word boundaries"))) and emits everything in-between. Its knowledge of Unicode allows it to successfully tokenize text containing a mixture of languages.

Punctuation may((("punctuation", "in words"))) or may not be considered part of a word, depending on where it appears:

[source,js]

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 uax_url_email tokenizer works((("uax_url_email tokenizer"))) in exactly the same way as the standard tokenizer, except that it recognizes((("email addresses and URLs, tokenizer for"))) email addresses and URLs and emits them as single tokens. The standard tokenizer, on the other hand, would try to break them into individual words. For instance, the email address joe-bloggs@foo-bar.com would result in the tokens joe, bloggs, foo, bar.com.

==================================================

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,((("icu_tokenizer"))) which is available in the ICU plug-in.