spaCy/website/usage/_linguistic-features/_rule-based-matching.jade

//- 💫 DOCS > USAGE > RULE-BASED MATCHING

p
    |  spaCy features a rule-matching engine, the #[+api("matcher") #[code Matcher]],
    |  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. To match large terminology lists,
    |  you can use the #[+api("phrasematcher") #[code PhraseMatcher]], which
    |  accepts #[code Doc] objects as match patterns.

+h(3, "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 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 lowercase 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
    pattern = [{'LOWER': 'hello'}, {'IS_PUNCT': True}, {'LOWER': 'world'}]
    matcher.add('HelloWorld', None, pattern)

    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', 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(4, "adding-patterns-attributes") Available token attributes

p
    |  The available token pattern keys are uppercase versions of the
    |  #[+api("token#attributes") #[code Token] attributes]. The most relevant
    |  ones for rule-based matching are:

+table(["Attribute", "Description"])
    +row
        +cell #[code ORTH]
        +cell The exact verbatim text of a token.

    +row
        +cell.u-nowrap #[code LOWER]
        +cell The lowercase form of the token text.

    +row
        +cell.u-nowrap #[code IS_ALPHA], #[code IS_ASCII], #[code IS_DIGIT]
        +cell
            |  Token text consists of alphanumeric characters, ASCII characters,
            |  digits.

    +row
        +cell.u-nowrap #[code IS_LOWER], #[code IS_UPPER], #[code IS_TITLE]
        +cell Token text is in lowercase, uppercase, titlecase.

    +row
        +cell.u-nowrap #[code IS_PUNCT], #[code IS_SPACE], #[code IS_STOP]
        +cell Token is punctuation, whitespace, stop word.

    +row
        +cell.u-nowrap #[code LIKE_NUM], #[code LIKE_URL], #[code LIKE_EMAIL]
        +cell Token text resembles a number, URL, email.

    +row
        +cell.u-nowrap
            |  #[code POS], #[code TAG], #[code DEP], #[code LEMMA],
            |  #[code SHAPE]
        +cell
            |  The token's simple and extended part-of-speech tag, dependency
            |  label, lemma, shape.

+h(4, "adding-patterns-wildcard") Using wildcard token patterns
    +tag-new(2)

p
    |  While the token attributes offer many options to write highly specific
    |  patterns, you can also use an empty dictionary, #[code {}] as a wildcard
    |  representing #[strong any token]. This is useful if you know the context
    |  of what you're trying to match, but very little about the specific token
    |  and its characters. For example, let's say you're trying to extract
    |  people's user names from your data. All you know is that they are listed
    |  as "User name: {username}". The name itself may contain any character,
    |  but no whitespace – so you'll know it will be handled as one token.

+code.
    [{'ORTH': 'User'}, {'ORTH': 'name'}, {'ORTH': ':'}, {}]

+h(4, "quantifiers") Using operators and quantifiers

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.

+table([ "OP", "Description"])
    +row
        +cell #[code !]
        +cell Negate the pattern, by requiring it to match exactly 0 times.

    +row
        +cell #[code ?]
        +cell Make the pattern optional, by allowing it to match 0 or 1 times.

    +row
        +cell #[code +]
        +cell Require the pattern to match 1 or more times.

    +row
        +cell #[code *]
        +cell Allow the pattern to match zero or more times.

p
    |  The #[code +] and #[code *] operators are usually interpretted
    |  "greedily", i.e. longer matches are returned where possible.

+h(3, "adding-phrase-patterns") Adding phrase patterns

p
    |  If you need to match large terminology lists, you can also use the
    |  #[+api("phrasematcher") #[code PhraseMatcher]] and create
    |  #[+api("doc") #[code Doc]] objects instead of token patterns, which is
    |  much more efficient overall. The #[code Doc] patterns can contain single
    |  or multiple tokens.

+code.
    import spacy
    from spacy.matcher import PhraseMatcher

    nlp = spacy.load('en')
    matcher = PhraseMatcher(nlp.vocab)
    terminology_list = ['Barack Obama', 'Angela Merkel', 'Washington, D.C.']
    patterns = [nlp(text) for text in terminology_list]
    matcher.add('TerminologyList', None, *patterns)

    doc = nlp(u"German Chancellor Angela Merkel and US President Barack Obama "
              u"converse in the Oval Office inside the White House in Washington, D.C.")
    matches = matcher(doc)

p
    |  Since spaCy is used for processing both the patterns and the text to be
    |  matched, you won't have to worry about specific tokenization – for
    |  example, you can simply pass in #[code nlp(u"Washington, D.C.")] and
    |  won't have to write a complex token pattern covering the exact
    |  tokenization of the term.

+h(3, "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)

    # 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),)

    matcher.add('GoogleIO', add_event_ent,
                [{'ORTH': 'Google'}, {'ORTH': 'I'}, {'ORTH': '/'}, {'ORTH': 'O'}],
                [{'ORTH': 'Google'}, {'ORTH': 'I'}, {'ORTH': '/'}, {'ORTH': '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
    |  (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.
    # 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 = nlp.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

    matcher.add('BAD_HTML', merge_and_flag,
                [{'ORTH': '<'}, {'LOWER': 'br'}, {'ORTH': '>'}],
                [{'ORTH': '<'}, {'LOWER': 'br/'}, {'ORTH': '>'}])

+aside("Tip: Visualizing matches")
    |  When working with entities, you can use #[+api("top-level#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 guide on
    |  #[+a("/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. 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)

p
    |  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]].

+h(3, "regex") Using regular expressions

p
    |  In some cases, only matching tokens and token attributes isn't enough –
    |  for example, you might want to match different spellings of a word,
    |  without having to add a new pattern for each spelling. A simple solution
    |  is to match a regular expression on the #[code Doc]'s #[code text] and
    |  use the #[+api("doc#char_span") #[code Doc.char_span]] method to
    |  create a #[code Span] from the character indices of the match:

+code.
    import spacy
    import re

    nlp = spacy.load('en')
    doc = nlp(u'The spelling is "definitely", not "definately" or "deffinitely".')

    DEFINITELY_PATTERN = re.compile(r'deff?in[ia]tely')

    for match in re.finditer(DEFINITELY_PATTERN, doc.text):
        start, end = match.span()         # get matched indices
        span = doc.char_span(start, end)  # create Span from indices

p
    |  You can also use the regular expression with spaCy's #[code Matcher] by
    |  converting it to a token flag. To ensure efficiency, the
    |  #[code Matcher] can only access the C-level data. This means that it can
    |  either use built-in token attributes or #[strong binary flags].
    |  #[+api("vocab#add_flag") #[code Vocab.add_flag]] returns a flag ID which
    |  you can use as a key of a token match pattern. Tokens that match the
    |  regular expression will return #[code True] for the #[code IS_DEFINITELY]
    |  flag.

+code.
    IS_DEFINITELY = nlp.vocab.add_flag(re.compile(r'deff?in[ia]tely').match)

    matcher = Matcher(nlp.vocab)
    matcher.add('DEFINITELY', None, [{IS_DEFINITELY: True}])

p
    |  Providing the regular expressions as binary flags also lets you use them
    |  in combination with other token patterns – for example, to match the
    |  word "definitely" in various spellings, followed by a case-insensitive
    |  "not" and and adjective:

+code.
    [{IS_DEFINITELY: True}, {'LOWER': 'not'}, {'POS': 'ADJ'}]

+h(3, "example1") Example: Using linguistic annotations

p
    |  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("/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("/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_char - sent.start_char,
                       'end': span.end_char - sent.start_char,
                       '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, "example2") 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.

+h(3, "example3") Example: Hashtags and emoji on social media

p
    |  Social media posts, especially tweets, can be difficult to work with.
    |  They're very short and often contain various emoji and hashtags. By only
    |  looking at the plain text, you'll lose a lot of valuable semantic
    |  information.

p
    |  Let's say you've extracted a large sample of social media posts on a
    |  specific topic, for example posts mentioning a brand name or product.
    |  As the first step of your data exploration, you want to filter out posts
    |  containing certain emoji and use them to assign a general sentiment
    |  score, based on whether the expressed emotion is positive or negative,
    |  e.g. #[span.o-icon.o-icon--inline 😀] or #[span.o-icon.o-icon--inline 😞].
    |  You also want to find, merge and label hashtags like
    |  #[code #MondayMotivation], to be able to ignore or analyse them later.

+aside("Note on sentiment analysis")
    |  Ultimately, sentiment analysis is not always #[em that] easy. In
    |  addition to the emoji, you'll also want to take specific words into
    |  account and check the #[code subtree] for intensifiers like "very", to
    |  increase the sentiment score. At some point, you might also want to train
    |  a sentiment model. However, the approach described in this example is
    |  very useful for #[strong bootstrapping rules to collect training data].
    |  It's also an incredibly fast way to gather first insights into your data
    |  – with about 1 million tweets, you'd be looking at a processing time of
    |  #[strong under 1 minute].

p
    |  By default, spaCy's tokenizer will split emoji into separate tokens. This
    |  means that you can create a pattern for one or more emoji tokens.
    |  Valid hashtags usually consist of a #[code #], plus a sequence of
    |  ASCII characters with no whitespace, making them easy to match as well.

+code.
    from spacy.lang.en import English
    from spacy.matcher import Matcher

    nlp = English() # we only want the tokenizer, so no need to load a model
    matcher = Matcher(nlp.vocab)

    pos_emoji = [u'😀', u'😃', u'😂', u'🤣', u'😊', u'😍'] # positive emoji
    neg_emoji = [u'😞', u'😠', u'😩', u'😢', u'😭', u'😒'] # negative emoji

    # add patterns to match one or more emoji tokens
    pos_patterns = [[{'ORTH': emoji}] for emoji in pos_emoji]
    neg_patterns = [[{'ORTH': emoji}] for emoji in neg_emoji]

    matcher.add('HAPPY', label_sentiment, *pos_patterns) # add positive pattern
    matcher.add('SAD', label_sentiment, *neg_patterns) # add negative pattern

    # add pattern to merge valid hashtag, i.e. '#' plus any ASCII token
    matcher.add('HASHTAG', merge_hashtag, [{'ORTH': '#'}, {'IS_ASCII': True}])

p
    |  Because the #[code on_match] callback receives the ID of each match, you
    |  can use the same function to handle the sentiment assignment for both
    |  the positive and negative pattern. To keep it simple, we'll either add
    |  or subtract #[code 0.1] points – this way, the score will also reflect
    |  combinations of emoji, even positive #[em and] negative ones.

p
    |  With a library like
    |  #[+a("https://github.com/bcongdon/python-emojipedia") Emojipedia],
    |  we can also retrieve a short description for each emoji – for example,
    |  #[span.o-icon.o-icon--inline 😍]'s official title is "Smiling Face With
    |  Heart-Eyes". Assigning it to the merged token's norm will make it
    |  available as #[code token.norm_].

+code.
    from emojipedia import Emojipedia # installation: pip install emojipedia

    def label_sentiment(matcher, doc, i, matches):
        match_id, start, end = matches[i]
        if doc.vocab.strings[match_id] == 'HAPPY': # don't forget to get string!
            doc.sentiment += 0.1 # add 0.1 for positive sentiment
        elif doc.vocab.strings[match_id] == 'SAD':
            doc.sentiment -= 0.1 # subtract 0.1 for negative sentiment
        span = doc[start : end]
        emoji = Emojipedia.search(span[0].text) # get data for emoji
        span.merge(norm=emoji.title) # merge span and set NORM to emoji title

p
    |  To label the hashtags, we first need to add a new custom flag.
    |  #[code IS_HASHTAG] will be the flag's ID, which you can use to assign it
    |  to the hashtag's span, and check its value via a token's
    |  #[+api("token#check_flag") #[code check_flag()]] method. On each
    |  match, we merge the hashtag and assign the flag.

+code.
    # Add a new custom flag to the vocab, which is always False by default
    IS_HASHTAG = nlp.vocab.add_flag(lambda text: False)

    def merge_hashtag(matcher, doc, i, matches):
        match_id, start, end = matches[i]
        span = doc[start : end]
        span.merge() # merge hashtag
        span.set_flag(IS_HASHTAG, True) # set IS_HASHTAG to True

p
    |  To process a stream of social media posts, we can use
    |  #[+api("language#pipe") #[code Language.pipe()]], which will return a
    |  stream of #[code Doc] objects that we can pass to
    |  #[+api("matcher#pipe") #[code Matcher.pipe()]].

+code.
    docs = nlp.pipe(LOTS_OF_TWEETS)
    matches = matcher.pipe(docs)