spaCy/website/docs/usage/101/_tokenization.md

During processing, spaCy first tokenizes the text, i.e. segments it into words, punctuation and so on. This is done by applying rules specific to each language. For example, punctuation at the end of a sentence should be split off – whereas "U.K." should remain one token. Each Doc consists of individual tokens, and we can iterate over them:

### {executable="true"}
import spacy

nlp = spacy.load("en_core_web_sm")
doc = nlp("Apple is looking at buying U.K. startup for $1 billion")
for token in doc:
    print(token.text)

0	1	2	3	4	5	6	7	8	9	10
Apple	is	looking	at	buying	U.K.	startup	for	$	1	billion

First, the raw text is split on whitespace characters, similar to text.split(' '). Then, the tokenizer processes the text from left to right. On each substring, it performs two checks:

1. Does the substring match a tokenizer exception rule? For example, "don't" does not contain whitespace, but should be split into two tokens, "do" and "n't", while "U.K." should always remain one token.

2. Can a prefix, suffix or infix be split off? For example punctuation like commas, periods, hyphens or quotes.

If there's a match, the rule is applied and the tokenizer continues its loop, starting with the newly split substrings. This way, spaCy can split complex, nested tokens like combinations of abbreviations and multiple punctuation marks.

• Tokenizer exception: Special-case rule to split a string into several tokens or prevent a token from being split when punctuation rules are applied.
• Prefix: Character(s) at the beginning, e.g. $, (, “, ¿.
• Suffix: Character(s) at the end, e.g. km, ), ”, !.
• Infix: Character(s) in between, e.g. -, --, /, ….

While punctuation rules are usually pretty general, tokenizer exceptions strongly depend on the specifics of the individual language. This is why each available language has its own subclass, like English or German, that loads in lists of hard-coded data and exception rules.