spaCy/website/docs/api/matcher.md

title	teaser	tag	source
Matcher	Match sequences of tokens, based on pattern rules	class	spacy/matcher/matcher.pyx

The Matcher lets you find words and phrases using rules describing their token attributes. Rules can refer to token annotations (like the text or part-of-speech tags), as well as lexical attributes like Token.is_punct. Applying the matcher to a Doc gives you access to the matched tokens in context. For in-depth examples and workflows for combining rules and statistical models, see the usage guide on rule-based matching.

Pattern format

### Example
[
  {"LOWER": "i"},
  {"LEMMA": {"IN": ["like", "love"]}},
  {"POS": "NOUN", "OP": "+"}
]

A pattern added to the Matcher consists of a list of dictionaries. Each dictionary describes one token and its attributes. The available token pattern keys correspond to a number of Token attributes. The supported attributes for rule-based matching are:

Attribute	Description
ORTH	The exact verbatim text of a token. str
TEXT	The exact verbatim text of a token. str
NORM	The normalized form of the token text. str
LOWER	The lowercase form of the token text. str
LENGTH	The length of the token text. int
IS_ALPHA, IS_ASCII, IS_DIGIT	Token text consists of alphabetic characters, ASCII characters, digits. bool
IS_LOWER, IS_UPPER, IS_TITLE	Token text is in lowercase, uppercase, titlecase. bool
IS_PUNCT, IS_SPACE, IS_STOP	Token is punctuation, whitespace, stop word. bool
IS_SENT_START	Token is start of sentence. bool
LIKE_NUM, LIKE_URL, LIKE_EMAIL	Token text resembles a number, URL, email. bool
SPACY	Token has a trailing space. bool
POS, TAG, MORPH, DEP, LEMMA, SHAPE	The token's simple and extended part-of-speech tag, morphological analysis, dependency label, lemma, shape. str
ENT_TYPE	The token's entity label. str
ENT_IOB	The IOB part of the token's entity tag. str
ENT_ID	The token's entity ID (ent_id). str
ENT_KB_ID	The token's entity knowledge base ID (ent_kb_id). str
_	Properties in custom extension attributes. Dict[str, Any]
OP	Operator or quantifier to determine how often to match a token pattern. str

Operators and quantifiers define how often a token pattern should be matched:

### Example
[
  {"POS": "ADJ", "OP": "*"},
  {"POS": "NOUN", "OP": "+"}
  {"POS": "PROPN", "OP": "{2}"}
]

OP	Description
!	Negate the pattern, by requiring it to match exactly 0 times.
?	Make the pattern optional, by allowing it to match 0 or 1 times.
+	Require the pattern to match 1 or more times.
*	Allow the pattern to match 0 or more times.
{n}	Require the pattern to match exactly n times.
{n,m}	Require the pattern to match at least n but not more than m times.
{n,}	Require the pattern to match at least n times.
{,m}	Require the pattern to match at most m times.

Token patterns can also map to a dictionary of properties instead of a single value to indicate whether the expected value is a member of a list or how it compares to another value.

### Example
[
  {"LEMMA": {"IN": ["like", "love", "enjoy"]}},
  {"POS": "PROPN", "LENGTH": {">=": 10}},
]

Attribute	Description
IN	Attribute value is member of a list. Any
NOT_IN	Attribute value is not member of a list. Any
IS_SUBSET	Attribute value (for MORPH or custom list attributes) is a subset of a list. Any
IS_SUPERSET	Attribute value (for MORPH or custom list attributes) is a superset of a list. Any
INTERSECTS	Attribute value (for MORPH or custom list attribute) has a non-empty intersection with a list. Any
==, >=, <=, >, <	Attribute value is equal, greater or equal, smaller or equal, greater or smaller. Union[int, float]

Matcher.__init__

Create the rule-based Matcher. If validate=True is set, all patterns added to the matcher will be validated against a JSON schema and a MatchPatternError is raised if problems are found. Those can include incorrect types (e.g. a string where an integer is expected) or unexpected property names.

Example

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

Name	Description
vocab	The vocabulary object, which must be shared with the documents the matcher will operate on. Vocab
validate	Validate all patterns added to this matcher. bool

Matcher.__call__

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

Note that if a single label has multiple patterns associated with it, the returned matches don't provide a way to tell which pattern was responsible for the match.

Example

from spacy.matcher import Matcher

matcher = Matcher(nlp.vocab)
pattern = [{"LOWER": "hello"}, {"LOWER": "world"}]
matcher.add("HelloWorld", [pattern])
doc = nlp("hello world!")
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
allow_missing 3	Whether to skip checks for missing annotation for attributes included in patterns. Defaults to False. bool
with_alignments 3.0.6	Return match alignment information as part of the match tuple as List[int] with the same length as the matched span. Each entry denotes the corresponding index of the token in the pattern. If as_spans is set to True, this setting is ignored. 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]]

Matcher.__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 = Matcher(nlp.vocab)
assert len(matcher) == 0
matcher.add("Rule", [[{"ORTH": "test"}]])
assert len(matcher) == 1

Name	Description
RETURNS	The number of rules. int

Matcher.__contains__

Check whether the matcher contains rules for a match ID.

Example

matcher = Matcher(nlp.vocab)
assert "Rule" not in matcher
matcher.add("Rule", [[{'ORTH': 'test'}]])
assert "Rule" in matcher

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

Matcher.add

Add a rule to the matcher, consisting of an ID key, one or more patterns, and an 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 = Matcher(nlp.vocab)
patterns = [
   [{"LOWER": "hello"}, {"LOWER": "world"}],
   [{"ORTH": "Google"}, {"ORTH": "Maps"}]
]
matcher.add("TEST_PATTERNS", patterns, on_match=on_match)
doc = nlp("HELLO WORLD on Google Maps.")
matches = matcher(doc)

Name	Description
match_id	An ID for the thing you're matching. str
patterns	Match pattern. A pattern consists of a list of dicts, where each dict describes a token. List[List[Dict[str, Any]]]
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
greedy 3	Optional filter for greedy matches. Can either be "FIRST" or "LONGEST". Optional[str]

Matcher.remove

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

Example

matcher.add("Rule", [[{"ORTH": "test"}]])
assert "Rule" in matcher
matcher.remove("Rule")
assert "Rule" not in matcher

Name	Description
key	The ID of the match rule. str

Matcher.get

Retrieve the pattern stored for a key. Returns the rule as an (on_match, patterns) tuple containing the callback and available patterns.

Example

matcher.add("Rule", [[{"ORTH": "test"}]])
on_match, patterns = matcher.get("Rule")

Name	Description
key	The ID of the match rule. str
RETURNS	The rule, as an (on_match, patterns) tuple. Tuple[Optional[Callable], List[List[dict]]]