spaCy/website/docs/usage/_spacy-101/_named-entities.jade
2017-05-23 23:16:31 +02:00

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//- 💫 DOCS > USAGE > SPACY 101 > NAMED ENTITIES
p
| A named entity is a "real-world object" that's assigned a name for
| example, a person, a country, a product or a book title. spaCy can
| #[strong recognise] #[+a("/docs/api/annotation#named-entities") various types]
| of named entities in a document, by asking the model for a
| #[strong prediction]. Because models are statistical and strongly depend
| on the examples they were trained on, this doesn't always work
| #[em perfectly] and might need some tuning later, depending on your use
| case.
p
| Named entities are available as the #[code ents] property of a #[code Doc]:
+code.
doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
for ent in doc.ents:
print(ent.text, ent.start_char, ent.end_char, ent.label_)
+aside
| #[strong Text]: The original entity text.#[br]
| #[strong Start]: Index of start of entity in the #[code Doc].#[br]
| #[strong End]: Index of end of entity in the #[code Doc].#[br]
| #[strong Label]: Entity label, i.e. type.
+table(["Text", "Start", "End", "Label", "Description"])
- var style = [0, 1, 1, 1, 0]
+annotation-row(["Apple", 0, 5, "ORG", "Companies, agencies, institutions."], style)
+annotation-row(["U.K.", 27, 31, "GPE", "Geopolitical entity, i.e. countries, cities, states."], style)
+annotation-row(["$1 billion", 44, 54, "MONEY", "Monetary values, including unit."], style)
p
| Using spaCy's built-in #[+a("/docs/usage/visualizers") displaCy visualizer],
| here's what our example sentence and its named entities look like:
+codepen("2f2ad1408ff79fc6a326ea3aedbb353b", 160)