mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 12:18:04 +03:00
216 lines
8.2 KiB
Plaintext
216 lines
8.2 KiB
Plaintext
//- 💫 DOCS > USAGE > RULE-BASED MATCHING
|
||
|
||
include ../../_includes/_mixins
|
||
|
||
p
|
||
| spaCy features a rule-matching engine that operates over tokens, similar
|
||
| to regular expressions. The rules can refer to token annotations (e.g.
|
||
| the token #[code text] or #[code tag_], and flags (e.g. #[code IS_PUNCT]).
|
||
| The rule matcher also lets you pass in a custom callback
|
||
| to act on matches – for example, to merge entities and apply custom labels.
|
||
| You can also associate patterns with entity IDs, to allow some basic
|
||
| entity linking or disambiguation.
|
||
|
||
+aside("What about \"real\" regular expressions?")
|
||
|
||
+h(2, "adding-patterns") Adding patterns
|
||
|
||
p
|
||
| Let's say we want to enable spaCy to find a combination of three tokens:
|
||
|
||
+list("numbers")
|
||
+item
|
||
| A token whose #[strong lower-case form matches "hello"], e.g. "Hello"
|
||
| or "HELLO".
|
||
+item
|
||
| A token whose #[strong #[code is_punct] flag is set to #[code True]],
|
||
| i.e. any punctuation.
|
||
+item
|
||
| A token whose #[strong lower-case form matches "world"], e.g. "World"
|
||
| or "WORLD".
|
||
|
||
+code.
|
||
[{'LOWER': 'hello'}, {'IS_PUNCT': True}, {'LOWER': 'world'}]
|
||
|
||
p
|
||
| First, we initialise the #[code Matcher] with a vocab. The matcher must
|
||
| always share the same vocab with the documents it will operate on. We
|
||
| can now call #[+api("matcher#add") #[code matcher.add()]] with an ID and
|
||
| our custom pattern. The second argument lets you pass in an optional
|
||
| callback function to invoke on a successful match. For now, we set it
|
||
| to #[code None].
|
||
|
||
+code.
|
||
import spacy
|
||
from spacy.matcher import Matcher
|
||
|
||
nlp = spacy.load('en')
|
||
matcher = Matcher(nlp.vocab)
|
||
# add match ID "HelloWorld" with no callback and one pattern
|
||
matcher.add('HelloWorld', on_match=None,
|
||
[{'LOWER': 'hello'}, {'IS_PUNCT': True}, {'LOWER': 'world'}])
|
||
|
||
doc = nlp(u'Hello, world! Hello world!')
|
||
matches = matcher(doc)
|
||
|
||
p
|
||
| The matcher returns a list of #[code (match_id, start, end)] tuples – in
|
||
| this case, #[code [('HelloWorld', 0, 2)]], which maps to the span
|
||
| #[code doc[0:2]] of our original document. Optionally, we could also
|
||
| choose to add more than one pattern, for example to also match sequences
|
||
| without punctuation between "hello" and "world":
|
||
|
||
+code.
|
||
matcher.add('HelloWorld', on_match=None,
|
||
[{'LOWER': 'hello'}, {'IS_PUNCT': True}, {'LOWER': 'world'}],
|
||
[{'LOWER': 'hello'}, {'LOWER': 'world'}])
|
||
|
||
p
|
||
| By default, the matcher will only return the matches and
|
||
| #[strong not do anything else], like merge entities or assign labels.
|
||
| This is all up to you and can be defined individually for each pattern,
|
||
| by passing in a callback function as the #[code on_match] argument on
|
||
| #[code add()]. This is useful, because it lets you write entirely custom
|
||
| and #[strong pattern-specific logic]. For example, you might want to
|
||
| merge #[em some] patterns into one token, while adding entity labels for
|
||
| other pattern types. You shouldn't have to create different matchers for
|
||
| each of those processes.
|
||
|
||
+h(2, "on_match") Adding #[code on_match] rules
|
||
|
||
p
|
||
| To move on to a more realistic example, let's say you're working with a
|
||
| large corpus of blog articles, and you want to match all mentions of
|
||
| "Google I/O" (which spaCy tokenizes as #[code ['Google', 'I', '/', 'O']]).
|
||
| To be safe, you only match on the uppercase versions, in case someone has
|
||
| written it as "Google i/o". You also add a second pattern with an added
|
||
| #[code {IS_DIGIT: True}] token – this will make sure you also match on
|
||
| "Google I/O 2017". If your pattern matches, spaCy should execute your
|
||
| custom callback function #[code add_event_ent].
|
||
|
||
+code.
|
||
import spacy
|
||
from spacy.matcher import Matcher
|
||
|
||
nlp = spacy.load('en')
|
||
matcher = Matcher(nlp.vocab)
|
||
|
||
matcher.add('GoogleIO', on_match=add_event_ent,
|
||
[{'ORTH': 'Google'}, {'UPPER': 'I'}, {'ORTH': '/'}, {'UPPER': 'O'}],
|
||
[{'ORTH': 'Google'}, {'UPPER': 'I'}, {'ORTH': '/'}, {'UPPER': 'O'}, {'IS_DIGIT': True}])
|
||
|
||
# Get the ID of the 'EVENT' entity type. This is required to set an entity.
|
||
EVENT = nlp.vocab.strings['EVENT']
|
||
|
||
def add_event_ent(matcher, doc, i, matches):
|
||
# Get the current match and create tuple of entity label, start and end.
|
||
# Append entity to the doc's entity. (Don't overwrite doc.ents!)
|
||
match_id, start, end = matches[i]
|
||
doc.ents += ((EVENT, start, end),)
|
||
|
||
p
|
||
| In addition to mentions of "Google I/O", your data also contains some
|
||
| annoying pre-processing artefacts, like leftover HTML line breaks
|
||
| (e.g. #[code <br>] or #[code <BR/>]). While you're at it,
|
||
| you want to merge those into one token and flag them, to make sure you
|
||
| can easily ignore them later. So you add a second pattern and pass in a
|
||
| function #[code merge_and_flag]:
|
||
|
||
+code.
|
||
matcher.add('BAD_HTML', on_match=merge_and_flag,
|
||
[{'ORTH': '<'}, {'LOWER': 'br'}, {'ORTH': '>'}],
|
||
[{'ORTH': '<'}, {'LOWER': 'br/'}, {'ORTH': '>'}])
|
||
|
||
# Add a new custom flag to the vocab, which is always False by default.
|
||
# BAD_HTML_FLAG will be the flag ID, which we can use to set it to True on the span.
|
||
BAD_HTML_FLAG = doc.vocab.add_flag(lambda text: False)
|
||
|
||
def merge_and_flag(matcher, doc, i, matches):
|
||
match_id, start, end = matches[i]
|
||
span = doc[start : end]
|
||
span.merge(is_stop=True) # merge (and mark it as a stop word, just in case)
|
||
span.set_flag(BAD_HTML_FLAG, True) # set BAD_HTML_FLAG
|
||
|
||
+aside("Tip: Visualizing matches")
|
||
| When working with entities, you can use #[+api("displacy") displaCy]
|
||
| to quickly generate a NER visualization from your updated #[code Doc],
|
||
| which can be exported as an HTML file:
|
||
|
||
+code.o-no-block.
|
||
from spacy import displacy
|
||
html = displacy.render(doc, style='ent', page=True,
|
||
options={'ents': ['EVENT']})
|
||
|
||
| For more info and examples, see the usage workflow on
|
||
| #[+a("/docs/usage/visualizers") visualizing spaCy].
|
||
|
||
p
|
||
| We can now call the matcher on our documents. The patterns will be
|
||
| matched in the order they occur in the text.
|
||
|
||
+code.
|
||
doc = nlp(LOTS_OF_TEXT)
|
||
matcher(doc)
|
||
|
||
+h(3, "on_match-callback") The callback function
|
||
|
||
p
|
||
| The matcher will first collect all matches over the document. It will
|
||
| then iterate over the matches, lookup the callback for the entity ID
|
||
| that was matched, and invoke it. When the callback is invoked, it is
|
||
| passed four arguments: the matcher itself, the document, the position of
|
||
| the current match, and the total list of matches. This allows you to
|
||
| write callbacks that consider the entire set of matched phrases, so that
|
||
| you can resolve overlaps and other conflicts in whatever way you prefer.
|
||
|
||
+table(["Argument", "Type", "Description"])
|
||
+row
|
||
+cell #[code matcher]
|
||
+cell #[code Matcher]
|
||
+cell The matcher instance.
|
||
|
||
+row
|
||
+cell #[code doc]
|
||
+cell #[code Doc]
|
||
+cell The document the matcher was used on.
|
||
|
||
+row
|
||
+cell #[code i]
|
||
+cell int
|
||
+cell Index of the current match (#[code matches[i]]).
|
||
|
||
+row
|
||
+cell #[code matches]
|
||
+cell list
|
||
+cell
|
||
| A list of #[code (match_id, start, end)] tuples, describing the
|
||
| matches. A match tuple describes a span #[code doc[start:end]].
|
||
| The #[code match_id] is the ID of the added match pattern.
|
||
|
||
+h(2, "quantifiers") Using quantifiers
|
||
|
||
+table([ "Name", "Description", "Example"])
|
||
+row
|
||
+cell #[code !]
|
||
+cell match exactly 0 times
|
||
+cell negation
|
||
|
||
+row
|
||
+cell #[code *]
|
||
+cell match 0 or more times
|
||
+cell optional, variable number
|
||
|
||
+row
|
||
+cell #[code +]
|
||
+cell match 1 or more times
|
||
+cell mandatory, variable number
|
||
|
||
+row
|
||
+cell #[code ?]
|
||
+cell match 0 or 1 times
|
||
+cell optional, max one
|
||
|
||
p
|
||
| There are no nested or scoped quantifiers. You can build those
|
||
| behaviours with #[code on_match] callbacks.
|