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EntityRuler with blank spaCy model

EntityRuler lets you to add entities to doc.ents. It can be combined with EntityRecognizer, a spaCy pipeline component for named-entity recognition, to boost accuracy, or used on its own to implement a purely rule-based entity recognition system. In this exercise, you will practice adding an EntityRuler component to a blank spaCy English model and classify named entities of the given text using purely rule-based named-entity recognition.

The spaCy package is already imported and a blank spaCy English model is ready for your use as nlp. A list of patterns to classify lower cased OpenAI and Microsoft as ORG is already created for your use.

Cet exercice fait partie du cours

Natural Language Processing with spaCy

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Instructions

  • Create and add an EntityRuler component to the pipeline.
  • Add given patterns to the EntityRuler component.
  • Run the model on the given text and create its corresponding Doc container.
  • Print a tuple of (entities text and types) for all entities in the Doc container

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

nlp = spacy.blank("en")
patterns = [{"label": "ORG", "pattern": [{"LOWER": "openai"}]},
            {"label": "ORG", "pattern": [{"LOWER": "microsoft"}]}]
text = "OpenAI has joined forces with Microsoft."

# Add EntityRuler component to the model
entity_ruler = nlp.____("entity_ruler")

# Add given patterns to the EntityRuler component
entity_ruler.____(____)

# Run the model on a given text
doc = nlp(____)

# Print entities text and type for all entities in the Doc container
print([(ent.____, ent.____) for ent in doc.____])
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