A transition-based dependency parser component. The dependency parser jointly learns sentence segmentation and labelled dependency parsing, and can optionally learn to merge tokens that had been over-segmented by the tokenizer. The parser uses a variant of the non-monotonic arc-eager transition-system described by Honnibal and Johnson (2014), with the addition of a “break” transition to perform the sentence segmentation. Nivre (2005)’s pseudo-projective dependency transformation is used to allow the parser to predict non-projective parses.

The parser is trained using an imitation learning objective. It follows the actions predicted by the current weights, and at each state, determines which actions are compatible with the optimal parse that could be reached from the current state. The weights are updated such that the scores assigned to the set of optimal actions is increased, while scores assigned to other actions are decreased. Note that more than one action may be optimal for a given state.

Assigned Attributes

Dependency predictions are assigned to the Token.dep and Token.head fields. Beside the dependencies themselves, the parser decides sentence boundaries, which are saved in Token.is_sent_start and accessible via Doc.sents.

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/dep_parser.pyx

DependencyParser.__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.

DependencyParser.__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.

DependencyParser.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.

DependencyParser.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 and lets you customize arguments it receives via the [initialize.components] block in the config.

DependencyParser.predict method

Apply the component’s model to a batch of Doc objects, without modifying them.

DependencyParser.set_annotations method

Modify a batch of Doc objects, using pre-computed scores.

DependencyParser.update method

Learn from a batch of Example objects, updating the pipe’s model. Delegates to predict and get_loss.

DependencyParser.get_loss method

Find the loss and gradient of loss for the batch of documents and their predicted scores.

DependencyParser.create_optimizer method

Create an Optimizer for the pipeline component.

DependencyParser.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.

DependencyParser.add_label method

Add a new label to the pipe. Note that you don’t have to call this method if you provide a representative data sample to the initialize method. In this case, all labels found in the sample will be automatically added to the model, and the output dimension will be inferred automatically.

DependencyParser.set_output method

Change the output dimension of the component’s model by calling the model’s attribute resize_output. This is a function that takes the original model and the new output dimension nO, and changes the model in place. When resizing an already trained model, care should be taken to avoid the “catastrophic forgetting” problem.

DependencyParser.to_disk method

Serialize the pipe to disk.

DependencyParser.from_disk method

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

DependencyParser.to_bytes method

Serialize the pipe to a bytestring.

DependencyParser.from_bytes method

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

DependencyParser.labels property

The labels currently added to the component.

DependencyParser.label_data propertyv3.0

The labels currently added to the component and their internal meta information. This is the data generated by init labels and used by DependencyParser.initialize to initialize the model with a pre-defined label set.

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.