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Improve regex matching docs [ci skip]

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Ines Montani 2019-08-19 13:59:41 +02:00
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website/docs/usage/rule-based-matching.md
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@ -249,37 +249,61 @@ pattern = [{"TEXT": {"REGEX": "^[Uu](\\.?|nited)$"}},
{"LOWER": "president"}]
```
`'REGEX'` as an operator (instead of a top-level property that only matches on
the token's text) allows defining rules for any string value, including custom
attributes:
The `REGEX` operator allows defining rules for any attribute string value,
including custom attributes. It always needs to be applied to an attribute like
`TEXT`, `LOWER` or `TAG`:
```python
# Match different spellings of token texts
pattern = [{"TEXT": {"REGEX": "deff?in[ia]tely"}}]
# Match tokens with fine-grained POS tags starting with 'V'
pattern = [{"TAG": {"REGEX": "^V"}}]
# Match custom attribute values with regular expressions
pattern = [{"_": {"country": {"REGEX": "^[Uu](\\.?|nited) ?[Ss](\\.?|tates)$"}}}]
pattern = [{"_": {"country": {"REGEX": "^[Uu](nited|\\.?) ?[Ss](tates|\\.?)$"}}}]
```
<Infobox title="Regular expressions in older versions" variant="warning">
<Infobox title="Important note" variant="warning">
Versions before v2.1.0 don't yet support the `REGEX` operator. A simple solution
is to match a regular expression on the `Doc.text` with `re.finditer` and use
the [`Doc.char_span`](/api/doc#char_span) method to create a `Span` from the
character indices of the match.
You can also use the regular expression by converting it to a **binary token
flag**. [`Vocab.add_flag`](/api/vocab#add_flag) returns a flag ID which you can
use as a key of a token match pattern.
```python
definitely_flag = lambda text: bool(re.compile(r"deff?in[ia]tely").match(text))
IS_DEFINITELY = nlp.vocab.add_flag(definitely_flag)
pattern = [{IS_DEFINITELY: True}]
```
When using the `REGEX` operator, keep in mind that it operates on **single
tokens**, not the whole text. Each expression you provide will be matched on a
token. If you need to match on the whole text instead, see the details on
[regex matching on the whole text](#regex-text).
</Infobox>
##### Matching regular expressions on the full text {#regex-text}
If your expressions apply to multiple tokens, a simple solution is to match on
the `doc.text` with `re.finditer` and use the
[`Doc.char_span`](/api/doc#char_span) method to create a `Span` from the
character indices of the match. If the matched characters don't map to one or
more valid tokens, `Doc.char_span` returns `None`.
> #### What's a valid token sequence?
>
> In the example, the expression will also match `"US"` in `"USA"`. However,
> `"USA"` is a single token and `Span` objects are **sequences of tokens**. So
> `"US"` cannot be its own span, because it does not end on a token boundary.
```python
### {executable="true"}
import spacy
import re
nlp = spacy.load("en_core_web_sm")
doc = nlp("The United States of America (USA) are commonly known as the United States (U.S. or US) or America.")
expression = r"[Uu](nited|\\.?) ?[Ss](tates|\\.?)"
for match in re.finditer(expression, doc.text):
start, end = match.span()
span = doc.char_span(start, end)
# This is a Span object or None if match doesn't map to valid token sequence
if span is not None:
print("Found match:", span.text)
```
#### Operators and quantifiers {#quantifiers}
The matcher also lets you use quantifiers, specified as the `'OP'` key.