spaCy/website/usage/_spacy-101/_tokenization.jade

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2017-05-24 00:16:31 +03:00
//- 💫 DOCS > USAGE > SPACY 101 > TOKENIZATION
p
| During processing, spaCy first #[strong 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 #[code Doc] consists of individual tokens, and we can simply iterate
| over them:
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💫 Interactive code examples, spaCy Universe and various docs improvements (#2274) * Integrate Python kernel via Binder * Add live model test for languages with examples * Update docs and code examples * Adjust margin (if not bootstrapped) * Add binder version to global config * Update terminal and executable code mixins * Pass attributes through infobox and section * Hide v-cloak * Fix example * Take out model comparison for now * Add meta text for compat * Remove chart.js dependency * Tidy up and simplify JS and port big components over to Vue * Remove chartjs example * Add Twitter icon * Add purple stylesheet option * Add utility for hand cursor (special cases only) * Add transition classes * Add small option for section * Add thumb object for small round thumbnail images * Allow unset code block language via "none" value (workaround to still allow unset language to default to DEFAULT_SYNTAX) * Pass through attributes * Add syntax highlighting definitions for Julia, R and Docker * Add website icon * Remove user survey from navigation * Don't hide GitHub icon on small screens * Make top navigation scrollable on small screens * Remove old resources page and references to it * Add Universe * Add helper functions for better page URL and title * Update site description * Increment versions * Update preview images * Update mentions of resources * Fix image * Fix social images * Fix problem with cover sizing and floats * Add divider and move badges into heading * Add docstrings * Reference converting section * Add section on converting word vectors * Move converting section to custom section and fix formatting * Remove old fastText example * Move extensions content to own section Keep weird ID to not break permalinks for now (we don't want to rewrite URLs if not absolutely necessary) * Use better component example and add factories section * Add note on larger model * Use better example for non-vector * Remove similarity in context section Only works via small models with tensors so has always been kind of confusing * Add note on init-model command * Fix lightning tour examples and make excutable if possible * Add spacy train CLI section to train * Fix formatting and add video * Fix formatting * Fix textcat example description (resolves #2246) * Add dummy file to try resolve conflict * Delete dummy file * Tidy up [ci skip] * Ensure sufficient height of loading container * Add loading animation to universe * Update Thebelab build and use better startup message * Fix asset versioning * Fix typo [ci skip] * Add note on project idea label
2018-04-29 03:06:46 +03:00
+code-exec.
import spacy
nlp = spacy.load('en_core_web_sm')
doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
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for token in doc:
print(token.text)
+table([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).u-text-center
+row
for cell in ["Apple", "is", "looking", "at", "buying", "U.K.", "startup", "for", "$", "1", "billion"]
+cell=cell
p
2017-06-24 13:31:33 +03:00
| First, the raw text is split on whitespace characters, similar to
| #[code text.split(' ')]. Then, the tokenizer processes the text from
| left to right. On each substring, it performs two checks:
+list("numbers")
+item
| #[strong 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.
+item
2017-06-01 13:47:36 +03:00
| #[strong Can a prefix, suffix or infix be split off?] For example
| punctuation like commas, periods, hyphens or quotes.
p
| 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
| #[strong complex, nested tokens] like combinations of abbreviations and
| multiple punctuation marks.
+aside
| #[strong Tokenizer exception:] Special-case rule to split a string into
| several tokens or prevent a token from being split when punctuation rules
| are applied.#[br]
| #[strong Prefix:] Character(s) at the beginning, e.g.
| #[code $], #[code (], #[code “], #[code ¿].#[br]
| #[strong Suffix:] Character(s) at the end, e.g.
| #[code km], #[code )], #[code ”], #[code !].#[br]
| #[strong Infix:] Character(s) in between, e.g.
| #[code -], #[code --], #[code /], #[code …].#[br]
2017-10-03 15:26:20 +03:00
+graphic("/assets/img/tokenization.svg")
include ../../assets/img/tokenization.svg
p
| While punctuation rules are usually pretty general, tokenizer exceptions
| strongly depend on the specifics of the individual language. This is
2017-11-13 10:30:06 +03:00
| why each #[+a("/usage/models#languages") available language] has its
| own subclass like #[code English] or #[code German], that loads in lists
| of hard-coded data and exception rules.