mirror of https://github.com/explosion/spaCy.git
39 lines
1.7 KiB
Markdown
39 lines
1.7 KiB
Markdown
A named entity is a "real-world object" that's assigned a name – for example, a
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person, a country, a product or a book title. spaCy can **recognize various
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types of named entities in a document, by asking the model for a
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prediction**. Because models are statistical and strongly depend on the
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examples they were trained on, this doesn't always work _perfectly_ and might
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need some tuning later, depending on your use case.
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Named entities are available as the `ents` property of a `Doc`:
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```python
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### {executable="true"}
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import spacy
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nlp = spacy.load("en_core_web_sm")
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doc = nlp("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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```
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> - **Text:** The original entity text.
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> - **Start:** Index of start of entity in the `Doc`.
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> - **End:** Index of end of entity in the `Doc`.
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> - **Label:** Entity label, i.e. type.
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| Text | Start | End | Label | Description |
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| ----------- | :---: | :-: | ------- | ---------------------------------------------------- |
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| Apple | 0 | 5 | `ORG` | Companies, agencies, institutions. |
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| U.K. | 27 | 31 | `GPE` | Geopolitical entity, i.e. countries, cities, states. |
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| \$1 billion | 44 | 54 | `MONEY` | Monetary values, including unit. |
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Using spaCy's built-in [displaCy visualizer](/usage/visualizers), here's what
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our example sentence and its named entities look like:
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import DisplaCyEntHtml from 'images/displacy-ent1.html'; import { Iframe } from
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'components/embed'
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<Iframe title="displaCy visualization of entities" html={DisplaCyEntHtml} height={100} />
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