spaCy/website/docs/api/entityruler.md

title	tag	source	new
EntityRuler	class	spacy/pipeline.pyx	2.1

The EntityRuler lets you add spans to the Doc.ents using token-based rules or exact phrase matches. It can be combined with the statistical EntityRecognizer to boost accuracy, or used on its own to implement a purely rule-based entity recognition system. After initialization, the component is typically added to the processing pipeline using nlp.add_pipe.

EntityRuler.__init__

Initialize the entity ruler. If patterns are supplied here, they need to be a list of dictionaries with a "label" and "pattern" key. A pattern can either be a token pattern (list) or a phrase pattern (string). For example: {'label': 'ORG', 'pattern': 'Apple'}.

Example

# Construction via create_pipe
ruler = nlp.create_pipe("entityruler")

# Construction from class
from spacy.pipeline import EntityRuler
ruler = EntityRuler(nlp, overwrite_ents=True)

Name	Type	Description
nlp	Language	The shared nlp object to pass the vocab to the matchers and process phrase patterns.
patterns	iterable	Optional patterns to load in.
overwrite_ents	bool	If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. Defaults to False.
**cfg	-	Other config parameters. If pipeline component is loaded as part of a model pipeline, this will include all keyword arguments passed to spacy.load.
RETURNS	EntityRuler	The newly constructed object.

EntityRuler._\len__

The number of all patterns added to the entity ruler.

Example

ruler = EntityRuler(nlp)
assert len(ruler) == 0
ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
assert len(ruler) == 1

Name	Type	Description
RETURNS	int	The number of patterns.

EntityRuler.__contains__

Whether a label is present in the patterns.

Example

ruler = EntityRuler(nlp)
ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
assert "ORG" in ruler
assert not "PERSON" in ruler

Name	Type	Description
label	unicode	The label to check.
RETURNS	bool	Whether the entity ruler contains the label.

EntityRuler.__call__

Find matches in the Doc and add them to the doc.ents. Typically, this happens automatically after the component has been added to the pipeline using nlp.add_pipe. If the entity ruler was initialized with overwrite_ents=True, existing entities will be replaced if they overlap with the matches.

Example

ruler = EntityRuler(nlp)
ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
nlp.add_pipe(ruler)

doc = nlp("A text about Apple.")
ents = [(ent.text, ent.label_) for ent in doc.ents]
assert ents == [("Apple", "ORG")]

Name	Type	Description
doc	Doc	The Doc object to process, e.g. the Doc in the pipeline.
RETURNS	Doc	The modified Doc with added entities, if available.

EntityRuler.add_patterns

Add patterns to the entity ruler. A pattern can either be a token pattern (list of dicts) or a phrase pattern (string). For more details, see the usage guide on rule-based matching.

Example

patterns = [
    {"label": "ORG", "pattern": "Apple"},
    {"label": "GPE", "pattern": [{"lower": "san"}, {"lower": "francisco"}]}
]
ruler = EntityRuler(nlp)
ruler.add_patterns(patterns)

Name	Type	Description
patterns	list	The patterns to add.

EntityRuler.to_disk

Save the entity ruler patterns to a directory. The patterns will be saved as newline-delimited JSON (JSONL).

Example

ruler = EntityRuler(nlp)
ruler.to_disk('/path/to/rules.jsonl')

Name	Type	Description
path	unicode / Path	A path to a file, which will be created if it doesn't exist. Paths may be either strings or Path-like objects.

EntityRuler.from_disk

Load the entity ruler from a file. Expects a file containing newline-delimited JSON (JSONL) with one entry per line.

Example

ruler = EntityRuler(nlp)
ruler.from_disk('/path/to/rules.jsonl')

Name	Type	Description
path	unicode / Path	A path to a JSONL file. Paths may be either strings or Path-like objects.
RETURNS	EntityRuler	The modified EntityRuler object.

EntityRuler.to_bytes

Serialize the entity ruler patterns to a bytestring.

Example

ruler = EntityRuler(nlp)
ruler_bytes = ruler.to_bytes()

Name	Type	Description
RETURNS	bytes	The serialized patterns.

EntityRuler.from_bytes

Load the pipe from a bytestring. Modifies the object in place and returns it.

Example

ruler_bytes = ruler.to_bytes()
ruler = EntityRuler(nlp)
ruler.from_bytes(ruler_bytes)

Name	Type	Description
patterns_bytes	bytes	The bytestring to load.
RETURNS	EntityRuler	The modified EntityRuler object.

EntityRuler.labels

All labels present in the match patterns.

Name	Type	Description
RETURNS	tuple	The string labels.

EntityRuler.patterns

Get all patterns that were added to the entity ruler.

Name	Type	Description
RETURNS	list	The original patterns, one dictionary per pattern.

Attributes

Name	Type	Description
matcher	Matcher	The underlying matcher used to process token patterns.
phrase_matcher	PhraseMatcher	The underlying phrase matcher, used to process phrase patterns.
token_patterns	dict	The token patterns present in the entity ruler, keyed by label.
phrase_patterns	dict	The phrase patterns present in the entity ruler, keyed by label.