EntityRuler · spaCy API Documentation

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.

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`

explosion/spaCy/master/spacy/pipeline/entityruler.py
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EntityRuler.init method

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"}.

Name	Description
`nlp`	The shared nlp object to pass the vocab to the matchers and process phrase patterns. `Language`
`name` v3.0	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 methodv3.0

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__ method

The number of all patterns added to the entity ruler.

Name	Description
RETURNS	The number of patterns. `int`

EntityRuler.contains method

Whether a label is present in the patterns.

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

EntityRuler.call method

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 method

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 method

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.

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 method

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.

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 method

Serialize the entity ruler patterns to a bytestring.

Name	Description
RETURNS	The serialized patterns. `bytes`

EntityRuler.from_bytes method

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

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

EntityRuler.labels property

All labels present in the match patterns.

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

EntityRuler.ent_ids property

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

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

EntityRuler.patterns property

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]]`

Mar	APR	May
	21
2020	2021	2022

Pipeline

Config and implementation

EntityRuler.__init__ method

EntityRuler.initialize methodv3.0

EntityRuler._len__ method

EntityRuler.__contains__ method

EntityRuler.__call__ method

EntityRuler.add_patterns method

EntityRuler.to_disk method

EntityRuler.from_disk method

EntityRuler.to_bytes method

EntityRuler.from_bytes method

EntityRuler.labels property

EntityRuler.ent_ids property

EntityRuler.patterns property

Attributes

EntityRuler.init method

EntityRuler.contains method

EntityRuler.call method