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