Skip to content

Latest commit

 

History

History
61 lines (48 loc) · 2.89 KB

20_Standard_tokenizer.asciidoc

File metadata and controls

61 lines (48 loc) · 2.89 KB
Raw

standard Tokenizer

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 uax_url_email tokenizer works in exactly the same way as the standard tokenizer, except that it recognizes 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 [email protected] 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, which is available in the ICU plug-in.