Creating a custom named entity in spaCy

If spaCy's built-in named entities aren't enough, you can make your own using spaCy's EntityRuler() class.

EntityRuler() allows you to create your own entities to add to a spaCy pipeline.

You start by creating an instance of EntityRuler() and passing it the current pipeline, nlp.

You can then call add_patterns() on the instance and pass it a dictionary of the text pattern you'd like to label with an entity.

Once you've setup a pattern you can add it the nlp pipeline using add_pipe().

Since Acme is a technology company, you decide to tag the pattern "smartphone" with the "PRODUCT" entity tag.

spaCy has been imported and a doc already exists containing the transcribed text from call_4_channel_2.wav file).

Cet exercice fait partie du cours

Spoken Language Processing in Python

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Instructions

Import EntityRuler from spacy.pipeline.
Add "smartphone" as the value for the "pattern" key.
Add the EntityRuler() instance, ruler, to the nlp pipeline.
Print the entity attributes contained in doc.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Import EntityRuler class
from spacy.pipeline import ____

# Create EntityRuler instance
ruler = EntityRuler(nlp)

# Define pattern for new entity
ruler.add_patterns([{"label": "PRODUCT", "pattern": ____}])

# Update existing pipeline
nlp.add_pipe(____, before="ner")

# Test new entity
for entity in doc.____:
  print(entity.text, entity.label_)

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