A CoreferenceResolver component groups tokens into clusters that refer to the same thing. Clusters are represented as SpanGroups that start with a prefix (coref_clusters by default).

A CoreferenceResolver component can be paired with a SpanResolver to expand single tokens to spans.

Assigned Attributes

Predictions will be saved to Doc.spans as a SpanGroup. The span key will be a prefix plus a serial number referring to the coreference cluster, starting from zero.

The span key prefix defaults to "coref_clusters", but can be passed as a parameter.

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.

CoreferenceResolver.__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.

CoreferenceResolver.__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.

CoreferenceResolver.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.

CoreferenceResolver.initialize method

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.

CoreferenceResolver.predict method

Apply the component’s model to a batch of Doc objects, without modifying them. Clusters are returned as a list of MentionClusters, one for each input Doc. A MentionClusters instance is just a list of lists of pairs of ints, where each item corresponds to a cluster, and the ints correspond to token indices.

CoreferenceResolver.set_annotations method

Modify a batch of documents, saving coreference clusters in Doc.spans.

CoreferenceResolver.update method

Learn from a batch of Example objects. Delegates to predict.

CoreferenceResolver.create_optimizer method

Create an optimizer for the pipeline component.

CoreferenceResolver.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.

CoreferenceResolver.to_disk method

Serialize the pipe to disk.

CoreferenceResolver.from_disk method

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

CoreferenceResolver.to_bytes method

Serialize the pipe to a bytestring, including the KnowledgeBase.

CoreferenceResolver.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.