spaCy/website/docs/api/entitylinker.md

title	tag	source	new	teaser	api_base_class	api_string_name	api_trainable
EntityLinker	class	spacy/pipeline/entity_linker.py	2.2	Pipeline component for named entity linking and disambiguation	/api/pipe	entity_linker	true

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.

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.

Example

from spacy.pipeline.entity_linker import DEFAULT_NEL_MODEL
config = {
   "labels_discard": [],
   "incl_prior": True,
   "incl_context": True,
   "model": DEFAULT_NEL_MODEL,
   "kb_loader": {'@misc': 'spacy.EmptyKB.v1', 'entity_vector_length': 64},
   "get_candidates": {'@misc': 'spacy.CandidateGenerator.v1'},
}
nlp.add_pipe("entity_linker", config=config)

Setting	Description
labels_discard	NER labels that will automatically get a "NIL" prediction. Defaults to []. Iterable[str]
incl_prior	Whether or not to include prior probabilities from the KB in the model. Defaults to True. bool
incl_context	Whether or not to include the local context in the model. Defaults to True. bool
model	The Model powering the pipeline component. Defaults to EntityLinker. Model
kb_loader	Function that creates a KnowledgeBase from a Vocab instance. Defaults to EmptyKB, a function returning an empty KnowledgeBase with an entity_vector_length of 64. Callable[[Vocab], KnowledgeBase]
get_candidates	Function that generates plausible candidates for a given Span object. Defaults to CandidateGenerator, a function looking up exact, case-dependent aliases in the KB. CallableKnowledgeBase, Span], Iterable[Candidate

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

EntityLinker.__init__

Example

# Construction via add_pipe with default model
entity_linker = nlp.add_pipe("entity_linker")

# Construction via add_pipe with custom model
config = {"model": {"@architectures": "my_el.v1"}}
entity_linker = nlp.add_pipe("entity_linker", config=config)

# Construction via add_pipe with custom KB and candidate generation
config = {"kb": {"@misc": "my_kb.v1"}}
entity_linker = nlp.add_pipe("entity_linker", config=config)

# Construction from class
from spacy.pipeline import EntityLinker
entity_linker = EntityLinker(nlp.vocab, model)

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. Note that both the internal KnowledgeBase as well as the Candidate generator can be customized by providing custom registered functions.

Name	Description
vocab	The shared vocabulary. Vocab
model	The Model powering the pipeline component. Model
name	String name of the component instance. Used to add entries to the losses during training. str
keyword-only
kb_loader	Function that creates a KnowledgeBase from a Vocab instance. Callable[[Vocab], KnowledgeBase]
get_candidates	Function that generates plausible candidates for a given Span object. CallableKnowledgeBase, Span], Iterable[Candidate
labels_discard	NER labels that will automatically get a "NIL" prediction. Iterable[str]
incl_prior	Whether or not to include prior probabilities from the KB in the model. bool
incl_context	Whether or not to include the local context in the model. bool

EntityLinker.__call__

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.

Example

doc = nlp("This is a sentence.")
entity_linker = nlp.add_pipe("entity_linker")
# This usually happens under the hood
processed = entity_linker(doc)

Name	Description
doc	The document to process. Doc
RETURNS	The processed document. Doc

EntityLinker.pipe

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.

Example

entity_linker = nlp.add_pipe("entity_linker")
for doc in entity_linker.pipe(docs, batch_size=50):
    pass

Name	Description
stream	A stream of documents. Iterable[Doc]
keyword-only
batch_size	The number of documents to buffer. Defaults to 128. int
YIELDS	The processed documents in order. Doc

EntityLinker.begin_training

Initialize the component for training and return an Optimizer. get_examples should be a function that returns an iterable of Example objects. 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.

Example

entity_linker = nlp.add_pipe("entity_linker", last=True)
optimizer = entity_linker.begin_training(lambda: [], pipeline=nlp.pipeline)

Name	Description
get_examples	Function that returns gold-standard annotations in the form of Example objects. Callable], Iterable[Example
keyword-only
pipeline	Optional list of pipeline components that this component is part of. Optional[List[Tuple[str, CallableDoc], Doc]]
sgd	An optimizer. Will be created via create_optimizer if not set. Optional[Optimizer]
RETURNS	The optimizer. Optimizer

EntityLinker.predict

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.

Example

entity_linker = nlp.add_pipe("entity_linker")
kb_ids = entity_linker.predict([doc1, doc2])

Name	Description
docs	The documents to predict. Iterable[Doc]
RETURNS	List[str]

EntityLinker.set_annotations

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

Example

entity_linker = nlp.add_pipe("entity_linker")
kb_ids = entity_linker.predict([doc1, doc2])
entity_linker.set_annotations([doc1, doc2], kb_ids)

Name	Description
docs	The documents to modify. Iterable[Doc]
kb_ids	The knowledge base identifiers for the entities in the docs, predicted by EntityLinker.predict. List[str]

EntityLinker.update

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

Example

entity_linker = nlp.add_pipe("entity_linker")
optimizer = nlp.begin_training()
losses = entity_linker.update(examples, sgd=optimizer)

Name	Description
examples	A batch of Example objects to learn from. Iterable[Example]
keyword-only
drop	The dropout rate. float
set_annotations	Whether or not to update the Example objects with the predictions, delegating to set_annotations. bool
sgd	An optimizer. Will be created via create_optimizer if not set. Optional[Optimizer]
losses	Optional record of the loss during training. Updated using the component name as the key. Optional[Dict[str, float]]
RETURNS	The updated losses dictionary. Dict[str, float]

EntityLinker.create_optimizer

Create an optimizer for the pipeline component.

Example

entity_linker = nlp.add_pipe("entity_linker")
optimizer = entity_linker.create_optimizer()

Name	Description
RETURNS	The optimizer. Optimizer

EntityLinker.use_params

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

Example

entity_linker = nlp.add_pipe("entity_linker")
with entity_linker.use_params(optimizer.averages):
    entity_linker.to_disk("/best_model")

Name	Description
params	The parameter values to use in the model. dict

EntityLinker.to_disk

Serialize the pipe to disk.

Example

entity_linker = nlp.add_pipe("entity_linker")
entity_linker.to_disk("/path/to/entity_linker")

Name	Description
path	A 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
exclude	String names of serialization fields to exclude. Iterable[str]

EntityLinker.from_disk

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

Example

entity_linker = nlp.add_pipe("entity_linker")
entity_linker.from_disk("/path/to/entity_linker")

Name	Description
path	A path to a directory. Paths may be either strings or Path-like objects. Union[str, Path]
keyword-only
exclude	String names of serialization fields to exclude. Iterable[str]
RETURNS	The modified EntityLinker object. EntityLinker

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.

Example

data = entity_linker.to_disk("/path", exclude=["vocab"])

Name	Description
vocab	The shared Vocab.
cfg	The config file. You usually don't want to exclude this.
model	The binary model data. You usually don't want to exclude this.
kb	The knowledge base. You usually don't want to exclude this.