spaCy/website/docs/api/phrasematcher.md

title	teaser	tag	source	new
PhraseMatcher	Match sequences of tokens, based on documents	class	spacy/matcher/phrasematcher.pyx	2

The PhraseMatcher lets you efficiently match large terminology lists. While the Matcher lets you match sequences based on lists of token descriptions, the PhraseMatcher accepts match patterns in the form of Doc objects. See the usage guide for examples.

PhraseMatcher.__init__

Create the rule-based PhraseMatcher. Setting a different attr to match on will change the token attributes that will be compared to determine a match. By default, the incoming Doc is checked for sequences of tokens with the same ORTH value, i.e. the verbatim token text. Matching on the attribute LOWER will result in case-insensitive matching, since only the lowercase token texts are compared. In theory, it's also possible to match on sequences of the same part-of-speech tags or dependency labels.

If validate=True is set, additional validation is performed when pattern are added. At the moment, it will check whether a Doc has attributes assigned that aren't necessary to produce the matches (for example, part-of-speech tags if the PhraseMatcher matches on the token text). Since this can often lead to significantly worse performance when creating the pattern, a UserWarning will be shown.

Example

from spacy.matcher import PhraseMatcher
matcher = PhraseMatcher(nlp.vocab)

Name	Description
vocab	The vocabulary object, which must be shared with the documents the matcher will operate on. Vocab
attr	The token attribute to match on. Defaults to ORTH, i.e. the verbatim token text. Union[int, str]
validate	Validate patterns added to the matcher. bool

PhraseMatcher.__call__

Find all token sequences matching the supplied patterns on the Doc or Span.

Example

from spacy.matcher import PhraseMatcher

matcher = PhraseMatcher(nlp.vocab)
matcher.add("OBAMA", [nlp("Barack Obama")])
doc = nlp("Barack Obama lifts America one last time in emotional farewell")
matches = matcher(doc)

Name	Description
doclike	The Doc or Span to match over. Union[Doc, Span]
keyword-only
as_spans 3	Instead of tuples, return a list of Span objects of the matches, with the match_id assigned as the span label. Defaults to False. bool
RETURNS	A list of (match_id, start, end) tuples, describing the matches. A match tuple describes a span doc[start:end]. The match_id is the ID of the added match pattern. If as_spans is set to True, a list of Span objects is returned instead. Union[List[Tuple[int, int, int]], List[Span]]

Because spaCy stores all strings as integers, the match_id you get back will be an integer, too – but you can always get the string representation by looking it up in the vocabulary's StringStore, i.e. nlp.vocab.strings:

match_id_string = nlp.vocab.strings[match_id]

PhraseMatcher.__len__

Get the number of rules added to the matcher. Note that this only returns the number of rules (identical with the number of IDs), not the number of individual patterns.

Example

  matcher = PhraseMatcher(nlp.vocab)
  assert len(matcher) == 0
  matcher.add("OBAMA", [nlp("Barack Obama")])
  assert len(matcher) == 1

Name	Description
RETURNS	The number of rules. int

PhraseMatcher.__contains__

Check whether the matcher contains rules for a match ID.

Example

  matcher = PhraseMatcher(nlp.vocab)
  assert "OBAMA" not in matcher
  matcher.add("OBAMA", [nlp("Barack Obama")])
  assert "OBAMA" in matcher

Name	Description
key	The match ID. str
RETURNS	Whether the matcher contains rules for this match ID. bool

PhraseMatcher.add

Add a rule to the matcher, consisting of an ID key, one or more patterns, and a optional callback function to act on the matches. The callback function will receive the arguments matcher, doc, i and matches. If a pattern already exists for the given ID, the patterns will be extended. An on_match callback will be overwritten.

Example

  def on_match(matcher, doc, id, matches):
      print('Matched!', matches)

  matcher = PhraseMatcher(nlp.vocab)
  matcher.add("OBAMA", [nlp("Barack Obama")], on_match=on_match)
  matcher.add("HEALTH", [nlp("health care reform"), nlp("healthcare reform")], on_match=on_match)
  doc = nlp("Barack Obama urges Congress to find courage to defend his healthcare reforms")
  matches = matcher(doc)

Name	Description
key	An ID for the thing you're matching. str
docs	Doc objects of the phrases to match. List[Doc]
keyword-only
on_match	Callback function to act on matches. Takes the arguments matcher, doc, i and matches. Optional[CallableMatcher, Doc, int, List[tuple], Any

PhraseMatcher.remove

Remove a rule from the matcher by match ID. A KeyError is raised if the key does not exist.

Example

matcher = PhraseMatcher(nlp.vocab)
matcher.add("OBAMA", [nlp("Barack Obama")])
assert "OBAMA" in matcher
matcher.remove("OBAMA")
assert "OBAMA" not in matcher

Name	Description
key	The ID of the match rule. str