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  3. Spoken Language Processing in Python

Exercise

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.

Instructions

100 XP
  • 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.