spaCy/website/docs/api/entityruler.md
2020-07-27 18:11:45 +02:00

13 KiB

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.