spaCy/website/docs/api/entityruler.md

title	tag	source	new	teaser	api_string_name	api_trainable
EntityRuler	class	spacy/pipeline/entityruler.py	2.1	Pipeline component for rule-based named entity recognition	entity_ruler	false

The entity ruler 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. For usage examples, see the docs on rule-based entity recognition.

Config and implementation

The default config is defined by the pipeline component factory and describes how the component should be configured. You can override its settings via the config argument on nlp.add_pipe or in your config.cfg for training.

Example

config = {
   "phrase_matcher_attr": None,
   "validation": True,
   "overwrite_ents": False,
   "ent_id_sep": "||",
}
nlp.add_pipe("entity_ruler", config=config)

Setting	Type	Description	Default
phrase_matcher_attr	str	Optional attribute name match on for the internal PhraseMatcher, e.g. LOWER to match on the lowercase token text.	None
validation	bool	Whether patterns should be validated, passed to Matcher and PhraseMatcher as validate.	False
overwrite_ents	bool	If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary.	False
ent_id_sep	str	Separator used internally for entity IDs.	`"

https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/entityruler.py

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 add_pipe
ruler = nlp.add_pipe("entity_ruler")

# 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.
name 3	str	Instance name of the current pipeline component. Typically passed in automatically from the factory when the component is added. Used to disable the current entity ruler while creating phrase patterns with the nlp object.
keyword-only
phrase_matcher_attr	int / str	Optional attribute name match on for the internal PhraseMatcher, e.g. LOWER to match on the lowercase token text. Defaults to None.
validate	bool	Whether patterns should be validated, passed to Matcher and PhraseMatcher as validate. Defaults to False.
overwrite_ents	bool	If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. Defaults to False.
ent_id_sep	str	Separator used internally for entity IDs. Defaults to `"
patterns	iterable	Optional patterns to load in on initialization.

EntityRuler._\len__

The number of all patterns added to the entity ruler.

Example

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

Name	Type	Description
label	str	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. When matches overlap in a Doc, the entity ruler prioritizes longer patterns over shorter, and if equal the match occuring first in the Doc is chosen.

Example

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

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 = nlp.add_pipe("entity_ruler")
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). If a file with the suffix .jsonl is provided, only the patterns are saved as JSONL. If a directory name is provided, a patterns.jsonl and cfg file with the component configuration is exported.

Example

ruler = nlp.add_pipe("entity_ruler")
ruler.to_disk("/path/to/patterns.jsonl")  # saves patterns only
ruler.to_disk("/path/to/entity_ruler")    # saves patterns and config

Name	Type	Description
path	str / Path	A path to a JSONL file or directory, 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 either a file containing newline-delimited JSON (JSONL) with one entry per line, or a directory containing a patterns.jsonl file and a cfg file with the component configuration.

Example

ruler = nlp.add_pipe("entity_ruler")
ruler.from_disk("/path/to/patterns.jsonl")  # loads patterns only
ruler.from_disk("/path/to/entity_ruler")    # loads patterns and config

Name	Type	Description
path	str / Path	A path to a JSONL file or directory. 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 = nlp.add_pipe("entity_ruler")
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 = nlp.add_pipe("enity_ruler")
ruler.from_bytes(ruler_bytes)

Name	Type	Description
bytes_data	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.ent_ids

All entity ids present in the match patterns id properties.

Name	Type	Description
RETURNS	tuple	The string ent_ids.

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.