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323 lines
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323 lines
14 KiB
Plaintext
//- 💫 DOCS > USAGE > RULE-BASED MATCHING
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include ../../_includes/_mixins
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p
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| spaCy features a rule-matching engine that operates over tokens, similar
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| to regular expressions. The rules can refer to token annotations (e.g.
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| the token #[code text] or #[code tag_], and flags (e.g. #[code IS_PUNCT]).
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| The rule matcher also lets you pass in a custom callback
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| to act on matches – for example, to merge entities and apply custom labels.
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| You can also associate patterns with entity IDs, to allow some basic
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| entity linking or disambiguation.
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+aside("What about \"real\" regular expressions?")
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+h(2, "adding-patterns") Adding patterns
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p
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| Let's say we want to enable spaCy to find a combination of three tokens:
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+list("numbers")
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+item
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| A token whose #[strong lowercase form matches "hello"], e.g. "Hello"
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| or "HELLO".
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+item
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| A token whose #[strong #[code is_punct] flag is set to #[code True]],
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| i.e. any punctuation.
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+item
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| A token whose #[strong lowercase form matches "world"], e.g. "World"
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| or "WORLD".
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+code.
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[{'LOWER': 'hello'}, {'IS_PUNCT': True}, {'LOWER': 'world'}]
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p
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| First, we initialise the #[code Matcher] with a vocab. The matcher must
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| always share the same vocab with the documents it will operate on. We
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| can now call #[+api("matcher#add") #[code matcher.add()]] with an ID and
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| our custom pattern. The second argument lets you pass in an optional
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| callback function to invoke on a successful match. For now, we set it
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| to #[code None].
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+code.
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import spacy
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from spacy.matcher import Matcher
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nlp = spacy.load('en')
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matcher = Matcher(nlp.vocab)
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# add match ID "HelloWorld" with no callback and one pattern
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pattern = [{'LOWER': 'hello'}, {'IS_PUNCT': True}, {'LOWER': 'world'}]
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matcher.add('HelloWorld', None, pattern)
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doc = nlp(u'Hello, world! Hello world!')
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matches = matcher(doc)
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p
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| The matcher returns a list of #[code (match_id, start, end)] tuples – in
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| this case, #[code [('HelloWorld', 0, 2)]], which maps to the span
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| #[code doc[0:2]] of our original document. Optionally, we could also
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| choose to add more than one pattern, for example to also match sequences
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| without punctuation between "hello" and "world":
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+code.
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matcher.add('HelloWorld', None,
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[{'LOWER': 'hello'}, {'IS_PUNCT': True}, {'LOWER': 'world'}],
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[{'LOWER': 'hello'}, {'LOWER': 'world'}])
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p
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| By default, the matcher will only return the matches and
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| #[strong not do anything else], like merge entities or assign labels.
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| This is all up to you and can be defined individually for each pattern,
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| by passing in a callback function as the #[code on_match] argument on
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| #[code add()]. This is useful, because it lets you write entirely custom
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| and #[strong pattern-specific logic]. For example, you might want to
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| merge #[em some] patterns into one token, while adding entity labels for
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| other pattern types. You shouldn't have to create different matchers for
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| each of those processes.
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+h(2, "on_match") Adding #[code on_match] rules
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p
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| To move on to a more realistic example, let's say you're working with a
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| large corpus of blog articles, and you want to match all mentions of
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| "Google I/O" (which spaCy tokenizes as #[code ['Google', 'I', '/', 'O']]).
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| To be safe, you only match on the uppercase versions, in case someone has
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| written it as "Google i/o". You also add a second pattern with an added
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| #[code {IS_DIGIT: True}] token – this will make sure you also match on
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| "Google I/O 2017". If your pattern matches, spaCy should execute your
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| custom callback function #[code add_event_ent].
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+code.
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import spacy
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from spacy.matcher import Matcher
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nlp = spacy.load('en')
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matcher = Matcher(nlp.vocab)
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# Get the ID of the 'EVENT' entity type. This is required to set an entity.
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EVENT = nlp.vocab.strings['EVENT']
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def add_event_ent(matcher, doc, i, matches):
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# Get the current match and create tuple of entity label, start and end.
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# Append entity to the doc's entity. (Don't overwrite doc.ents!)
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match_id, start, end = matches[i]
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doc.ents += ((EVENT, start, end),)
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matcher.add('GoogleIO', add_event_ent,
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[{'ORTH': 'Google'}, {'UPPER': 'I'}, {'ORTH': '/'}, {'UPPER': 'O'}],
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[{'ORTH': 'Google'}, {'UPPER': 'I'}, {'ORTH': '/'}, {'UPPER': 'O'}, {'IS_DIGIT': True}])
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p
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| In addition to mentions of "Google I/O", your data also contains some
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| annoying pre-processing artefacts, like leftover HTML line breaks
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| (e.g. #[code <br>] or #[code <BR/>]). While you're at it,
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| you want to merge those into one token and flag them, to make sure you
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| can easily ignore them later. So you add a second pattern and pass in a
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| function #[code merge_and_flag]:
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+code.
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# Add a new custom flag to the vocab, which is always False by default.
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# BAD_HTML_FLAG will be the flag ID, which we can use to set it to True on the span.
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BAD_HTML_FLAG = doc.vocab.add_flag(lambda text: False)
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def merge_and_flag(matcher, doc, i, matches):
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match_id, start, end = matches[i]
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span = doc[start : end]
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span.merge(is_stop=True) # merge (and mark it as a stop word, just in case)
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span.set_flag(BAD_HTML_FLAG, True) # set BAD_HTML_FLAG
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matcher.add('BAD_HTML', merge_and_flag,
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[{'ORTH': '<'}, {'LOWER': 'br'}, {'ORTH': '>'}],
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[{'ORTH': '<'}, {'LOWER': 'br/'}, {'ORTH': '>'}])
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+aside("Tip: Visualizing matches")
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| When working with entities, you can use #[+api("displacy") displaCy]
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| to quickly generate a NER visualization from your updated #[code Doc],
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| which can be exported as an HTML file:
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+code.o-no-block.
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from spacy import displacy
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html = displacy.render(doc, style='ent', page=True,
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options={'ents': ['EVENT']})
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| For more info and examples, see the usage workflow on
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| #[+a("/docs/usage/visualizers") visualizing spaCy].
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p
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| We can now call the matcher on our documents. The patterns will be
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| matched in the order they occur in the text. The matcher will then
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| iterate over the matches, look up the callback for the match ID
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| that was matched, and invoke it.
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+code.
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doc = nlp(LOTS_OF_TEXT)
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matcher(doc)
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p
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| When the callback is invoked, it is
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| passed four arguments: the matcher itself, the document, the position of
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| the current match, and the total list of matches. This allows you to
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| write callbacks that consider the entire set of matched phrases, so that
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| you can resolve overlaps and other conflicts in whatever way you prefer.
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+table(["Argument", "Type", "Description"])
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+row
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+cell #[code matcher]
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+cell #[code Matcher]
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+cell The matcher instance.
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+row
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+cell #[code doc]
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+cell #[code Doc]
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+cell The document the matcher was used on.
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+row
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+cell #[code i]
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+cell int
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+cell Index of the current match (#[code matches[i]]).
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+row
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+cell #[code matches]
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+cell list
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+cell
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| A list of #[code (match_id, start, end)] tuples, describing the
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| matches. A match tuple describes a span #[code doc[start:end]].
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+h(2, "quantifiers") Using operators and quantifiers
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p
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| The matcher also lets you use quantifiers, specified as the #[code 'OP']
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| key. Quantifiers let you define sequences of tokens to be mached, e.g.
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| one or more punctuation marks, or specify optional tokens. Note that there
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| are no nested or scoped quantifiers – instead, you can build those
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| behaviours with #[code on_match] callbacks.
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+aside("Problems with quantifiers")
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| Using quantifiers may lead to unexpected results when matching
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| variable-length patterns, for example if the next token would also be
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| matched by the previous token. This problem should be resolved in a future
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| release. For more information, see
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| #[+a(gh("spaCy") + "/issues/864") this issue].
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+table([ "OP", "Description", "Example"])
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+row
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+cell #[code !]
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+cell match exactly 0 times
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+cell negation
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+row
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+cell #[code *]
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+cell match 0 or more times
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+cell optional, variable number
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+row
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+cell #[code +]
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+cell match 1 or more times
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+cell mandatory, variable number
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+row
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+cell #[code ?]
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+cell match 0 or 1 times
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+cell optional, max one
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+h(3, "quantifiers-example1") Quantifiers example: Using linguistic annotations
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p
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| Let's say you're analysing user comments and you want to find out what
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| people are saying about Facebook. You want to start off by finding
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| adjectives following "Facebook is" or "Facebook was". This is obviously
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| a very rudimentary solution, but it'll be fast, and a great way get an
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| idea for what's in your data. Your pattern could look like this:
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+code.
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[{'LOWER': 'facebook'}, {'LEMMA': 'be'}, {'POS': 'ADV', 'OP': '*'}, {'POS': 'ADJ'}]
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p
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| This translates to a token whose lowercase form matches "facebook"
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| (like Facebook, facebook or FACEBOOK), followed by a token with the lemma
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| "be" (for example, is, was, or 's), followed by an #[strong optional] adverb,
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| followed by an adjective. Using the linguistic annotations here is
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| especially useful, because you can tell spaCy to match "Facebook's
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| annoying", but #[strong not] "Facebook's annoying ads". The optional
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| adverb makes sure you won't miss adjectives with intensifiers, like
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| "pretty awful" or "very nice".
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p
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| To get a quick overview of the results, you could collect all sentences
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| containing a match and render them with the
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| #[+a("/docs/usage/visualizers") displaCy visualizer].
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| In the callback function, you'll have access to the #[code start] and
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| #[code end] of each match, as well as the parent #[code Doc]. This lets
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| you determine the sentence containing the match,
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| #[code doc[start : end].sent], and calculate the start and end of the
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| matched span within the sentence. Using displaCy in
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| #[+a("/docs/usage/visualizers#manual-usage") "manual" mode] lets you
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| pass in a list of dictionaries containing the text and entities to render.
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+code.
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from spacy import displacy
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from spacy.matcher import Matcher
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nlp = spacy.load('en')
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matcher = Matcher(nlp.vocab)
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matched_sents = [] # collect data of matched sentences to be visualized
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def collect_sents(matcher, doc, i, matches):
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match_id, start, end = matches[i]
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span = doc[start : end] # matched span
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sent = span.sent # sentence containing matched span
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# append mock entity for match in displaCy style to matched_sents
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# get the match span by ofsetting the start and end of the span with the
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# start and end of the sentence in the doc
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match_ents = [{'start': span.start-sent.start, 'end': span.end-sent.start,
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'label': 'MATCH'}]
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matched_sents.append({'text': sent.text, 'ents': match_ents })
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pattern = [{'LOWER': 'facebook'}, {'LEMMA': 'be'}, {'POS': 'ADV', 'OP': '*'},
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{'POS': 'ADJ'}]
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matcher.add('FacebookIs', collect_sents, pattern) # add pattern
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matches = matcher(nlp(LOTS_OF_TEXT)) # match on your text
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# serve visualization of sentences containing match with displaCy
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# set manual=True to make displaCy render straight from a dictionary
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displacy.serve(matched_sents, style='ent', manual=True)
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+h(3, "quantifiers-example2") Quantifiers example: Phone numbers
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p
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| Phone numbers can have many different formats and matching them is often
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| tricky. During tokenization, spaCy will leave sequences of numbers intact
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| and only split on whitespace and punctuation. This means that your match
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| pattern will have to look out for number sequences of a certain length,
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| surrounded by specific punctuation – depending on the
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| #[+a("https://en.wikipedia.org/wiki/National_conventions_for_writing_telephone_numbers") national conventions].
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p
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| The #[code IS_DIGIT] flag is not very helpful here, because it doesn't
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| tell us anything about the length. However, you can use the #[code SHAPE]
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| flag, with each #[code d] representing a digit:
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+code.
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[{'ORTH': '('}, {'SHAPE': 'ddd'}, {'ORTH': ')'}, {'SHAPE': 'dddd'},
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{'ORTH': '-', 'OP': '?'}, {'SHAPE': 'dddd'}]
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p
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| This will match phone numbers of the format #[strong (123) 4567 8901] or
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| #[strong (123) 4567-8901]. To also match formats like #[strong (123) 456 789],
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| you can add a second pattern using #[code 'ddd'] in place of #[code 'dddd'].
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| By hard-coding some values, you can match only certain, country-specific
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| numbers. For example, here's a pattern to match the most common formats of
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| #[+a("https://en.wikipedia.org/wiki/National_conventions_for_writing_telephone_numbers#Germany") international German numbers]:
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+code.
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[{'ORTH': '+'}, {'ORTH': '49'}, {'ORTH': '(', 'OP': '?'}, {'SHAPE': 'dddd'},
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{'ORTH': ')', 'OP': '?'}, {'SHAPE': 'dddddd'}]
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p
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| Depending on the formats your application needs to match, creating an
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| extensive set of rules like this is often better than training a model.
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| It'll produce more predictable results, is much easier to modify and
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| extend, and doesn't require any training data – only a set of
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| test cases.
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