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Rewrite rule-based matching workflow

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ines 2017-05-20 01:38:55 +02:00
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website/docs/usage/rule-based-matching.jade
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@ -4,58 +4,186 @@ 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 and
| flags, and matches support callbacks to accept, modify and/or act on the
| match. The rule matcher also allows you to associate patterns with
| entity IDs, to allow some basic entity linking or disambiguation.
| 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.
p Here's a minimal example. We first add a pattern that specifies three tokens:
+aside("What about \"real\" regular expressions?")
+list("numbers")
+item A token whose lower-case form matches "hello"
+item A token whose #[code is_punct] flag is set to #[code True]
+item A token whose lower-case form matches "world"
+h(2, "adding-patterns") Adding patterns
p
| Once we've added the pattern, we can use the #[code matcher] as a
| callable, to receive a list of #[code (ent_id, start, end)] tuples.
| Note that #[code LOWER] and #[code IS_PUNCT] are data attributes
| of #[code spacy.attrs].
| 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.
from spacy.matcher import Matcher
matcher = Matcher(nlp.vocab)
matcher.add_pattern("HelloWorld", [{LOWER: "hello"}, {IS_PUNCT: True}, {LOWER: "world"}])
[{LOWER: 'hello'}, {IS_PUNCT: True}, {LOWER: 'world'}]
doc = nlp(u'Hello, 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:
+code.
import spacy
from spacy.matcher import Matcher
from spacy.attrs import LOWER, IS_PUNCT # don't forget to import the attrs!
nlp = spacy.load('en')
matcher = Matcher(nlp.vocab)
matcher.add_pattern('HelloWorld', [{LOWER: 'hello'}, {IS_PUNCT: True}, {LOWER: 'world'}])
doc = nlp(u'Hello, world! Hello world!')
matches = matcher(doc)
p
| The returned matches include the ID, to let you associate the matches
| with the patterns. You can also group multiple patterns together, which
| is useful when you have a knowledge base of entities you want to match,
| and you want to write multiple patterns for each entity.
+h(2, "entities-patterns") Entities and patterns
| 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_entity(
"GoogleNow", # Entity ID -- Helps you act on the match.
{"ent_type": "PRODUCT", "wiki_en": "Google_Now"}, # Arbitrary attributes (optional)
)
matcher.add_pattern('HelloWorld', [{LOWER: 'hello'}, {IS_PUNCT: True}, {LOWER: 'world'}],
[{LOWER: 'hello'}, {LOWER: 'world'}])
matcher.add_pattern(
"GoogleNow", # Entity ID -- Created if doesn't exist.
[ # The pattern is a list of *Token Specifiers*.
{ # This Token Specifier matches tokens whose orth field is "Google"
ORTH: "Google"
},
{ # This Token Specifier matches tokens whose orth field is "Now"
ORTH: "Now"
}
],
label=None # Can associate a label to the pattern-match, to handle it better.
)
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 this pattern matches, spaCy should execute your
| custom callback function #[code add_event_ent].
+code.
import spacy
from spacy.matcher import Matcher
from spacy.attrs import ORTH, UPPER, LOWER, IS_DIGIT
nlp = spacy.load('en')
matcher = Matcher(nlp.vocab)
matcher.add_pattern('GoogleIO', [{ORTH: 'Google'}, {UPPER: 'I'}, {ORTH: '/'}, {UPPER: 'O'}],
[{ORTH: 'Google'}, {UPPER: 'I'}, {ORTH: '/'}, {UPPER: 'O'}, {IS_DIGIT: True}],
on_match=add_event_ent)
# 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, in case
# it already has other entities!)
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_pattern('BAD_HTML', [{ORTH: '<'}, {LOWER: 'br'}, {ORTH: '>'}],
[{ORTH: '<'}, {LOWER: 'br/'}, {ORTH: '>'}]
on_match=merge_and_flag)
# Add a new custom flag to the vocab, which is always False by default.
# BAD_HTML 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 the #[+api("displacy") displaCy]
| in your callback function to quickly generate a NER visualization
| from your updated #[code Doc], to export 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
@ -82,78 +210,4 @@ p
p
| There are no nested or scoped quantifiers. You can build those
| behaviours with acceptors and
| #[+api("matcher#add_entity") #[code on_match]] callbacks.
+h(2, "acceptor-functions") Acceptor functions
p
| The #[code acceptor] keyword of #[code matcher.add_entity()] allows you to
| pass a function to reject or modify matches. The function you pass should
| take five arguments: #[code doc], #[code ent_id], #[code label], #[code start],
| and #[code end]. You can return a falsey value to reject the match, or
| return a 4-tuple #[code (ent_id, label, start, end)].
+code.
from spacy.tokens.doc import Doc
def trim_title(doc, ent_id, label, start, end):
if doc[start].check_flag(IS_TITLE_TERM):
return (ent_id, label, start+1, end)
else:
return (ent_id, label, start, end)
titles = set(title.lower() for title in [u'Mr.', 'Dr.', 'Ms.', u'Admiral'])
IS_TITLE_TERM = matcher.vocab.add_flag(lambda string: string.lower() in titles)
matcher.add_entity('PersonName', acceptor=trim_title)
matcher.add_pattern('PersonName', [{LOWER: 'mr.'}, {LOWER: 'cruise'}])
matcher.add_pattern('PersonName', [{LOWER: 'dr.'}, {LOWER: 'seuss'}])
doc = Doc(matcher.vocab, words=[u'Mr.', u'Cruise', u'likes', 'Dr.', u'Seuss'])
for ent_id, label, start, end in matcher(doc):
print(doc[start:end].text)
# Cruise
# Seuss
p
| Passing an #[code acceptor] function allows you to match patterns with
| arbitrary logic that can't easily be expressed by a finite-state machine.
| You can look at the entirety of the
| matched phrase, and its context in the document, and decide to move
| the boundaries or reject the match entirely.
+h(2, "callback-functions") Callback functions
p
| In spaCy <1.0, the #[code Matcher] automatically tagged matched phrases
| with entity types. Since spaCy 1.0, the matcher no longer acts on matches
| automatically. By default, the match list is returned for the user to action.
| However, it's often more convenient to register the required actions as a
| callback. You can do this by passing a function to the #[code on_match]
| keyword argument of #[code matcher.add_entity].
+aside-code("Example").
def merge_phrases(matcher, doc, i, matches):
'''
Merge a phrase. We have to be careful here because we'll change the token indices.
To avoid problems, merge all the phrases once we're called on the last match.
'''
if i != len(matches)-1:
return None
# Get Span objects
spans = [(ent_id, label, doc[start : end]) for ent_id, label, start, end in matches]
for ent_id, label, span in spans:
span.merge(label=label, tag='NNP' if label else span.root.tag_)
matcher.add_entity('GoogleNow', on_match=merge_phrases)
matcher.add_pattern('GoogleNow', [{ORTH: 'Google'}, {ORTH: 'Now'}])
doc = Doc(matcher.vocab, words=[u'Google', u'Now', u'is', u'being', u'rebranded'])
matcher(doc)
print([w.text for w in doc])
# [u'Google Now', u'is', u'being', u'rebranded']
p
| The matcher will first collect all matches over the document. It will
| then iterate over the matches, look-up 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.
| behaviours with #[code on_match] callbacks.