spaCy/website/docs/usage/rule-based-matching.jade

//- 💫 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:

+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('HelloWorld', [{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', [{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
    from spacy.attrs import ORTH, UPPER, LOWER, IS_DIGIT

    nlp = spacy.load('en')
    matcher = Matcher(nlp.vocab)

    matcher.add('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!)
        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', [{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_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.