Pipeline

EntityLinker

class
String name:entity_linkerTrainable:
Pipeline component for named entity linking and disambiguation

An EntityLinker component disambiguates textual mentions (tagged as named entities) to unique identifiers, grounding the named entities into the “real world”. It requires a KnowledgeBase, as well as a function to generate plausible candidates from that KnowledgeBase given a certain textual mention, and a machine learning model to pick the right candidate, given the local context of the mention. EntityLinker defaults to using the InMemoryLookupKB implementation.

Assigned Attributes

Predictions, in the form of knowledge base IDs, will be assigned to Token.ent_kb_id_.

LocationValue
Token.ent_kb_idKnowledge base ID (hash). int
Token.ent_kb_id_Knowledge base ID. 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. See the model architectures documentation for details on the architectures and their arguments and hyperparameters.

SettingDescription
labels_discardNER labels that will automatically get a “NIL” prediction. Defaults to []. Iterable[str]
n_sentsThe number of neighbouring sentences to take into account. Defaults to 0. int
incl_priorWhether or not to include prior probabilities from the KB in the model. Defaults to True. bool
incl_contextWhether or not to include the local context in the model. Defaults to True. bool
modelThe Model powering the pipeline component. Defaults to EntityLinker. Model
entity_vector_lengthSize of encoding vectors in the KB. Defaults to 64. int
use_gold_entsWhether to copy entities from the gold docs or not. Defaults to True. If False, entities must be set in the training data or by an annotating component in the pipeline. int
get_candidatesFunction that generates plausible candidates for a given Span object. Defaults to CandidateGenerator, a function looking up exact, case-dependent aliases in the KB. Callable[[KnowledgeBase,Span], Iterable[Candidate]]
get_candidates_batch v3.5Function that generates plausible candidates for a given batch of Span objects. Defaults to CandidateBatchGenerator, a function looking up exact, case-dependent aliases in the KB. Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]]
generate_empty_kb v3.5.1Function that generates an empty KnowledgeBase object. Defaults to spacy.EmptyKB.v2, which generates an empty InMemoryLookupKB. Callable[[Vocab, int],KnowledgeBase]
overwrite v3.2Whether existing annotation is overwritten. Defaults to True. bool
scorer v3.2The scoring method. Defaults to Scorer.score_links. Optional[Callable]
threshold v3.4Confidence threshold for entity predictions. The default of None implies that all predictions are accepted, otherwise those with a score beneath the threshold are discarded. If there are no predictions with scores above the threshold, the linked entity is NIL. Optional[float]
explosion/spaCy/master/spacy/pipeline/entity_linker.py

EntityLinker.__init__ method

Create a new pipeline instance. In your application, you would normally use a shortcut for this and instantiate the component using its string name and nlp.add_pipe.

Upon construction of the entity linker component, an empty knowledge base is constructed with the provided entity_vector_length. If you want to use a custom knowledge base, you should either call set_kb or provide a kb_loader in the initialize call.

NameDescription
vocabThe shared vocabulary. Vocab
modelThe Model powering the pipeline component. Model
nameString name of the component instance. Used to add entries to the losses during training. str
keyword-only
entity_vector_lengthSize of encoding vectors in the KB. int
get_candidatesFunction that generates plausible candidates for a given Span object. Callable[[KnowledgeBase,Span], Iterable[Candidate]]
labels_discardNER labels that will automatically get a "NIL" prediction. Iterable[str]
n_sentsThe number of neighbouring sentences to take into account. int
incl_priorWhether or not to include prior probabilities from the KB in the model. bool
incl_contextWhether or not to include the local context in the model. bool
overwrite v3.2Whether existing annotation is overwritten. Defaults to True. bool
scorer v3.2The scoring method. Defaults to Scorer.score_links. Optional[Callable]
threshold v3.4Confidence threshold for entity predictions. The default of None implies that all predictions are accepted, otherwise those with a score beneath the threshold are discarded. If there are no predictions with scores above the threshold, the linked entity is NIL. Optional[float]

EntityLinker.__call__ method

Apply the pipe to one document. The document is modified in place and returned. This usually happens under the hood when the nlp object is called on a text and all pipeline components are applied to the Doc in order. Both __call__ and pipe delegate to the predict and set_annotations methods.

NameDescription
docThe document to process. Doc

EntityLinker.pipe method

Apply the pipe to a stream of documents. This usually happens under the hood when the nlp object is called on a text and all pipeline components are applied to the Doc in order. Both __call__ and pipe delegate to the predict and set_annotations methods.

NameDescription
streamA stream of documents. Iterable[Doc]
keyword-only
batch_sizeThe number of documents to buffer. Defaults to 128. int

EntityLinker.set_kb methodv3.0

The kb_loader should be a function that takes a Vocab instance and creates the KnowledgeBase, ensuring that the strings of the knowledge base are synced with the current vocab.

NameDescription
kb_loaderFunction that creates a KnowledgeBase from a Vocab instance. Callable[[Vocab],KnowledgeBase]

EntityLinker.initialize methodv3.0

Initialize the component for training. get_examples should be a function that returns an iterable of Example objects. At least one example should be supplied. The data examples are used to initialize the model of the component and can either be the full training data or a representative sample. Initialization includes validating the network, inferring missing shapes and setting up the label scheme based on the data. This method is typically called by Language.initialize.

Optionally, a kb_loader argument may be specified to change the internal knowledge base. This argument should be a function that takes a Vocab instance and creates the KnowledgeBase, ensuring that the strings of the knowledge base are synced with the current vocab.

NameDescription
get_examplesFunction that returns gold-standard annotations in the form of Example objects. Must contain at least one Example. Callable[[], Iterable[Example]]
keyword-only
nlpThe current nlp object. Defaults to None. Optional[Language]
kb_loaderFunction that creates a KnowledgeBase from a Vocab instance. Callable[[Vocab],KnowledgeBase]

EntityLinker.predict method

Apply the component’s model to a batch of Doc objects, without modifying them. Returns the KB IDs for each entity in each doc, including NIL if there is no prediction.

NameDescription
docsThe documents to predict. Iterable[Doc]

EntityLinker.set_annotations method

Modify a batch of documents, using pre-computed entity IDs for a list of named entities.

NameDescription
docsThe documents to modify. Iterable[Doc]
kb_idsThe knowledge base identifiers for the entities in the docs, predicted by EntityLinker.predict. List[str]

EntityLinker.update method

Learn from a batch of Example objects, updating both the pipe’s entity linking model and context encoder. Delegates to predict.

NameDescription
examplesA batch of Example objects to learn from. Iterable[Example]
keyword-only
dropThe dropout rate. float
sgdAn optimizer. Will be created via create_optimizer if not set. Optional[Optimizer]
lossesOptional record of the loss during training. Updated using the component name as the key. Optional[Dict[str, float]]

EntityLinker.create_optimizer method

Create an optimizer for the pipeline component.

NameDescription

EntityLinker.use_params methodcontextmanager

Modify the pipe’s model, to use the given parameter values. At the end of the context, the original parameters are restored.

NameDescription
paramsThe parameter values to use in the model. dict

EntityLinker.to_disk method

Serialize the pipe to disk.

NameDescription
pathA path to a directory, which will be created if it doesn’t exist. Paths may be either strings or Path-like objects. Union[str,Path]
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

EntityLinker.from_disk method

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

NameDescription
pathA path to a directory. Paths may be either strings or Path-like objects. Union[str,Path]
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

EntityLinker.to_bytes method

Serialize the pipe to a bytestring, including the KnowledgeBase.

NameDescription
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

EntityLinker.from_bytes method

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

NameDescription
bytes_dataThe data to load from. bytes
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

Serialization fields

During serialization, spaCy will export several data fields used to restore different aspects of the object. If needed, you can exclude them from serialization by passing in the string names via the exclude argument.

NameDescription
vocabThe shared Vocab.
cfgThe config file. You usually don’t want to exclude this.
modelThe binary model data. You usually don’t want to exclude this.
kbThe knowledge base. You usually don’t want to exclude this.