spacy-dbpedia-spotlight 0.1.3

SpaCy DBpedia Spotlight wrapper

Spacy DBpedia Spotlight

This package acts as a Entity Recogniser and Linker using DBpedia Spotlight, annotating SpaCy's Spans and adding them to the entities annotations.

It can create a new Language object or be added on an existing one.

It uses:

• DBPEDIA_ENT as ent_type
• the entity URI as ent_kb_id

Usage

Use on a blank new language

import spacy_dbpedia_spotlight

# here a new blank model will be created
nlp = spacy_dbpedia_spotlight.load('en')
doc = nlp('The president of USA is calling Boris Johnson to decide what to do about coronavirus')
print("Entities", [(ent.text, ent.label_, ent.kb_id_) for ent in doc.ents])

Or use on top of an existing nlp object (added as last pipeline stage)

import spacy
import spacy_dbpedia_spotlight

# use your model
nlp = spacy.load('en_core_web_lg')
# pass nlp as parameter
spacy_dbpedia_spotlight.load('en', nlp)
doc = nlp('The president of USA is calling Boris Johnson to decide what to do about coronavirus')
print("Entities", [(ent.text, ent.label_, ent.kb_id_) for ent in doc.ents])

Or if you want to just get the pipeline stage and place it where you want

import spacy

# any nlp you want
nlp = spacy.blank('en')
# create the pipe component, the dict argument is optional
entity_annotator = nlp.create_pipe('annotate_dbpedia_spotlight', {'language_code':'it'})
# add on your fancy pipeline with options like `first`
nlp.add_pipe(entity_annotator, first=True)

Output example:

Entities [('USA', 'DBPEDIA_ENT', 'http://dbpedia.org/resource/United_States'), ('Boris Johnson', 'DBPEDIA_ENT', 'http://dbpedia.org/resource/Boris_Johnson'), ('coronavirus', 'DBPEDIA_ENT', 'http://dbpedia.org/resource/Coronavirus')]