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_.

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.

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.

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.

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.

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.

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.

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.

EntityLinker.set_annotations method

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

EntityLinker.update method

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

EntityLinker.create_optimizer method

Create an optimizer for the pipeline component.

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.

EntityLinker.to_disk method

Serialize the pipe to disk.

EntityLinker.from_disk method

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

EntityLinker.to_bytes method

Serialize the pipe to a bytestring, including the KnowledgeBase.

EntityLinker.from_bytes method

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

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.