mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-31 16:07:41 +03:00
Update docs on rule-based matching and add examples
This commit is contained in:
ines
parent
701cba1524
commit
4cd26bcb83
|
|
@ -20,13 +20,13 @@ p
|
|||
|
||||
+list("numbers")
|
||||
+item
|
||||
| A token whose #[strong lower-case form matches "hello"], e.g. "Hello"
|
||||
| A token whose #[strong lowercase 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"
|
||||
| A token whose #[strong lowercase form matches "world"], e.g. "World"
|
||||
| or "WORLD".
|
||||
|
||||
+code.
|
||||
|
|
@ -95,10 +95,6 @@ p
|
|||
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']
|
||||
|
||||
|
|
@ -108,6 +104,10 @@ p
|
|||
match_id, start, end = matches[i]
|
||||
doc.ents += ((EVENT, start, end),)
|
||||
|
||||
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}])
|
||||
|
||||
p
|
||||
| In addition to mentions of "Google I/O", your data also contains some
|
||||
| annoying pre-processing artefacts, like leftover HTML line breaks
|
||||
|
|
@ -117,10 +117,6 @@ p
|
|||
| 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)
|
||||
|
|
@ -131,6 +127,10 @@ p
|
|||
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
|
||||
|
||||
matcher.add('BAD_HTML', on_match=merge_and_flag,
|
||||
[{'ORTH': '<'}, {'LOWER': 'br'}, {'ORTH': '>'}],
|
||||
[{'ORTH': '<'}, {'LOWER': 'br/'}, {'ORTH': '>'}])
|
||||
|
||||
+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],
|
||||
|
|
@ -146,18 +146,16 @@ p
|
|||
|
||||
p
|
||||
| We can now call the matcher on our documents. The patterns will be
|
||||
| matched in the order they occur in the text.
|
||||
| matched in the order they occur in the text. The matcher will then
|
||||
| iterate over the matches, look up the callback for the match ID
|
||||
| that was matched, and invoke it.
|
||||
|
||||
+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
|
||||
| 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
|
||||
|
|
@ -185,11 +183,24 @@ p
|
|||
+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
|
||||
+h(2, "quantifiers") Using operators and quantifiers
|
||||
|
||||
+table([ "Name", "Description", "Example"])
|
||||
p
|
||||
| The matcher also lets you use quantifiers, specified as the #[code 'OP']
|
||||
| key. Quantifiers let you define sequences of tokens to be mached, e.g.
|
||||
| one or more punctuation marks, or specify optional tokens. Note that there
|
||||
| are no nested or scoped quantifiers – instead, you can build those
|
||||
| behaviours with #[code on_match] callbacks.
|
||||
|
||||
+aside("Problems with quantifiers")
|
||||
| Using quantifiers may lead to unexpected results when matching
|
||||
| variable-length patterns, for example if the next token would also be
|
||||
| matched by the previous token. This problem should be resolved in a future
|
||||
| release. For more information, see
|
||||
| #[+a(gh("spaCy") + "/issues/864") this issue].
|
||||
|
||||
+table([ "OP", "Description", "Example"])
|
||||
+row
|
||||
+cell #[code !]
|
||||
+cell match exactly 0 times
|
||||
|
|
@ -210,6 +221,103 @@ p
|
|||
+cell match 0 or 1 times
|
||||
+cell optional, max one
|
||||
|
||||
+h(3, "quantifiers-example1") Quantifiers example: Using linguistic annotations
|
||||
|
||||
p
|
||||
| There are no nested or scoped quantifiers. You can build those
|
||||
| behaviours with #[code on_match] callbacks.
|
||||
| Let's say you're analysing user comments and you want to find out what
|
||||
| people are saying about Facebook. You want to start off by finding
|
||||
| adjectives following "Facebook is" or "Facebook was". This is obviously
|
||||
| a very rudimentary solution, but it'll be fast, and a great way get an
|
||||
| idea for what's in your data. Your pattern could look like this:
|
||||
|
||||
+code.
|
||||
[{'LOWER': 'facebook'}, {'LEMMA': 'be'}, {'POS': 'ADV', 'OP': '*'}, {'POS': 'ADJ'}]
|
||||
|
||||
p
|
||||
| This translates to a token whose lowercase form matches "facebook"
|
||||
| (like Facebook, facebook or FACEBOOK), followed by a token with the lemma
|
||||
| "be" (for example, is, was, or 's), followed by an #[strong optional] adverb,
|
||||
| followed by an adjective. Using the linguistic annotations here is
|
||||
| especially useful, because you can tell spaCy to match "Facebook's
|
||||
| annoying", but #[strong not] "Facebook's annoying ads". The optional
|
||||
| adverb makes sure you won't miss adjectives with intensifiers, like
|
||||
| "pretty awful" or "very nice".
|
||||
|
||||
p
|
||||
| To get a quick overview of the results, you could collect all sentences
|
||||
| containing a match and render them with the
|
||||
| #[+a("/docs/usage/visualizers") displaCy visualizer].
|
||||
| In the callback function, you'll have access to the #[code start] and
|
||||
| #[code end] of each match, as well as the parent #[code Doc]. This lets
|
||||
| you determine the sentence containing the match,
|
||||
| #[code doc[start : end].sent], and calculate the start and end of the
|
||||
| matched span within the sentence. Using displaCy in
|
||||
| #[+a("/docs/usage/visualizers#manual-usage") "manual" mode] lets you
|
||||
| pass in a list of dictionaries containing the text and entities to render.
|
||||
|
||||
+code.
|
||||
from spacy import displacy
|
||||
from spacy.matcher import Matcher
|
||||
|
||||
nlp = spacy.load('en')
|
||||
matcher = Matcher(nlp.vocab)
|
||||
matched_sents = [] # collect data of matched sentences to be visualized
|
||||
|
||||
def collect_sents(matcher, doc, i, matches):
|
||||
match_id, start, end = matches[i]
|
||||
span = doc[start : end] # matched span
|
||||
sent = span.sent # sentence containing matched span
|
||||
# append mock entity for match in displaCy style to matched_sents
|
||||
# get the match span by ofsetting the start and end of the span with the
|
||||
# start and end of the sentence in the doc
|
||||
match_ents = [{'start': span.start-sent.start, 'end': span.end-sent.start,
|
||||
'label': 'MATCH'}]
|
||||
matched_sents.append({'text': sent.text, 'ents': match_ents })
|
||||
|
||||
pattern = [{'LOWER': 'facebook'}, {'LEMMA': 'be'}, {'POS': 'ADV', 'OP': '*'},
|
||||
{'POS': 'ADJ'}]
|
||||
matcher.add('FacebookIs', collect_sents, pattern) # add pattern
|
||||
matches = matcher(nlp(LOTS_OF_TEXT)) # match on your text
|
||||
|
||||
# serve visualization of sentences containing match with displaCy
|
||||
# set manual=True to make displaCy render straight from a dictionary
|
||||
displacy.serve(matched_sents, style='ent', manual=True)
|
||||
|
||||
|
||||
+h(3, "quantifiers-example2") Quantifiers example: Phone numbers
|
||||
|
||||
p
|
||||
| Phone numbers can have many different formats and matching them is often
|
||||
| tricky. During tokenization, spaCy will leave sequences of numbers intact
|
||||
| and only split on whitespace and punctuation. This means that your match
|
||||
| pattern will have to look out for number sequences of a certain length,
|
||||
| surrounded by specific punctuation – depending on the
|
||||
| #[+a("https://en.wikipedia.org/wiki/National_conventions_for_writing_telephone_numbers") national conventions].
|
||||
|
||||
p
|
||||
| The #[code IS_DIGIT] flag is not very helpful here, because it doesn't
|
||||
| tell us anything about the length. However, you can use the #[code SHAPE]
|
||||
| flag, with each #[code d] representing a digit:
|
||||
|
||||
+code.
|
||||
[{'ORTH': '('}, {'SHAPE': 'ddd'}, {'ORTH': ')'}, {'SHAPE': 'dddd'},
|
||||
{'ORTH': '-', 'OP': '?'}, {'SHAPE': 'dddd'}]
|
||||
|
||||
p
|
||||
| This will match phone numbers of the format #[strong (123) 4567 8901] or
|
||||
| #[strong (123) 4567-8901]. To also match formats like #[strong (123) 456 789],
|
||||
| you can add a second pattern using #[code 'ddd'] in place of #[code 'dddd'].
|
||||
| By hard-coding some values, you can match only certain, country-specific
|
||||
| numbers. For example, here's a pattern to match the most common formats of
|
||||
| #[+a("https://en.wikipedia.org/wiki/National_conventions_for_writing_telephone_numbers#Germany") international German numbers]:
|
||||
|
||||
+code.
|
||||
[{'ORTH': '+'}, {'ORTH': '49'}, {'ORTH': '(', 'OP': '?'}, {'SHAPE': 'dddd'},
|
||||
{'ORTH': ')', 'OP': '?'}, {'SHAPE': 'dddddd'}]
|
||||
|
||||
p
|
||||
| Depending on the formats your application needs to match, creating an
|
||||
| extensive set of rules like this is often better than training a model.
|
||||
| It'll produce more predictable results, is much easier to modify and
|
||||
| extend, and doesn't require any training data – only a set of
|
||||
| test cases.
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user