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.

Assigned Attributes

This component assigns predictions basically the same way as the EntityRecognizer.

Predictions can be accessed under Doc.ents as a tuple. Each label will also be reflected in each underlying token, where it is saved in the Token.ent_type and Token.ent_iob fields. Note that by definition each token can only have one label.

When setting Doc.ents to create training data, all the spans must be valid and non-overlapping, or an error will be thrown.

Location	Value
Doc.ents	The annotated spans. Tuple[Span]
Token.ent_iob	An enum encoding of the IOB part of the named entity tag. int
Token.ent_iob_	The IOB part of the named entity tag. str
Token.ent_type	The label part of the named entity tag (hash). int
Token.ent_type_	The label part of the named entity tag. str

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,
   "validate": True,
   "overwrite_ents": False,
   "ent_id_sep": "||",
}
nlp.add_pipe("entity_ruler", config=config)

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

%%GITHUB_SPACY/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	Description
nlp	The shared nlp object to pass the vocab to the matchers and process phrase patterns. Language
name 3	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. str
keyword-only
phrase_matcher_attr	Optional attribute name match on for the internal PhraseMatcher, e.g. LOWER to match on the lowercase token text. Defaults to None. Optional[Union[int, str]]
validate	Whether patterns should be validated, passed to Matcher and PhraseMatcher as validate. Defaults to False. bool
overwrite_ents	If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. Defaults to False. bool
ent_id_sep	Separator used internally for entity IDs. Defaults to "||". str
patterns	Optional patterns to load in on initialization. Optional[List[Dict[str, Union[str, List[dict]]]]]

EntityRuler.initialize

Initialize the component with data and used before training to load in rules from a file. This method is typically called by Language.initialize and lets you customize arguments it receives via the [initialize.components] block in the config.

Example

entity_ruler = nlp.add_pipe("entity_ruler")
entity_ruler.initialize(lambda: [], nlp=nlp, patterns=patterns)

### config.cfg
[initialize.components.entity_ruler]

[initialize.components.entity_ruler.patterns]
@readers = "srsly.read_jsonl.v1"
path = "corpus/entity_ruler_patterns.jsonl

Name	Description
get_examples	Function that returns gold-standard annotations in the form of Example objects. Not used by the EntityRuler. Callable], Iterable[Example
keyword-only
nlp	The current nlp object. Defaults to None. Optional[Language]
patterns	The list of patterns. Defaults to None. Optional[Sequence[Dict[str, Union[str, List[Dict[str, Any]]]]]]

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	Description
RETURNS	The number of patterns. int

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	Description
label	The label to check. str
RETURNS	Whether the entity ruler contains the label. bool

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

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	Description
patterns	The patterns to add. List[Dict[str, Union[str, List[dict]]]]

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	Description
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. Union[str, Path]

EntityRuler.from_disk

Load the entity ruler from a path. 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	Description
path	A path to a JSONL file or directory. Paths may be either strings or Path-like objects. Union[str, Path]
RETURNS	The modified EntityRuler object. EntityRuler

EntityRuler.to_bytes

Serialize the entity ruler patterns to a bytestring.

Example

ruler = nlp.add_pipe("entity_ruler")
ruler_bytes = ruler.to_bytes()

Name	Description
RETURNS	The serialized patterns. bytes

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	Description
bytes_data	The bytestring to load. bytes
RETURNS	The modified EntityRuler object. EntityRuler

EntityRuler.labels

All labels present in the match patterns.

Name	Description
RETURNS	The string labels. Tuple[str, ...]

EntityRuler.ent_ids

All entity IDs present in the id properties of the match patterns.

Name	Description
RETURNS	The string IDs. Tuple[str, ...]

EntityRuler.patterns

Get all patterns that were added to the entity ruler.

Name	Description
RETURNS	The original patterns, one dictionary per pattern. List[Dict[str, Union[str, dict]]]

Attributes

Name	Description
matcher	The underlying matcher used to process token patterns. Matcher
phrase_matcher	The underlying phrase matcher used to process phrase patterns. PhraseMatcher
token_patterns	The token patterns present in the entity ruler, keyed by label. Dict[str, List[Dict[str, Union[str, List[dict]]]]
phrase_patterns	The phrase patterns present in the entity ruler, keyed by label. Dict[str, List[Doc]]