EntityRuler for NER
EntityRuler can be combined with EntityRecognizer of an existing model to boost its accuracy. In this exercise, you will practice combining an EntityRuler component and an existing NER component of the en_core_web_sm model. The model is already loaded as nlp.
When EntityRuler is added before NER component, the entity recognizer will respect the existing entity spans and adjust its predictions based on patterns added to the EntityRuler to improve accuracy of named entity recognition task.
This exercise is part of the course
Natural Language Processing with spaCy
Exercise instructions
- Add an
EntityRulerto thenlpbeforenercomponent. - Define a token entity pattern to classify lower cased
new york groupasORG. - Add the
patternsto theEntityRulercomponent. - Run the model and print the tuple of entities text and type for the
Doccontainer.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
nlp = spacy.load("en_core_web_sm")
text = "New York Group was built in 1987."
# Add an EntityRuler to the nlp before NER component
ruler = nlp.____("entity_ruler", ____="ner")
# Define a pattern to classify lower cased new york group as ORG
patterns = [{"label": "ORG", "pattern": [{"lower": ____}]}]
# Add the patterns to the EntityRuler component
ruler.____(____)
# Run the model and print entities text and type for all the entities
doc = ____
print([(ent.____, ent.____) for ent in doc.____])