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<!--- Provide a general summary of your changes in the title. --> ## Description The new website is implemented using [Gatsby](https://www.gatsbyjs.org) with [Remark](https://github.com/remarkjs/remark) and [MDX](https://mdxjs.com/). This allows authoring content in **straightforward Markdown** without the usual limitations. Standard elements can be overwritten with powerful [React](http://reactjs.org/) components and wherever Markdown syntax isn't enough, JSX components can be used. Hopefully, this update will also make it much easier to contribute to the docs. Once this PR is merged, I'll implement auto-deployment via [Netlify](https://netlify.com) on a specific branch (to avoid building the website on every PR). There's a bunch of other cool stuff that the new setup will allow us to do – including writing front-end tests, service workers, offline support, implementing a search and so on. This PR also includes various new docs pages and content. Resolves #3270. Resolves #3222. Resolves #2947. Resolves #2837. ### Types of change enhancement ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
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**
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[various types](/api/annotation#named-entities) of named entities in a document,
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by asking the model for a **prediction**. Because models are statistical and
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strongly depend on the examples they were trained on, this doesn't always work
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_perfectly_ and might 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(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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```
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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 DisplaCyLongHtml from 'images/displacy-long.html'; import { Iframe } from
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'components/embed'
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<Iframe title="displaCy visualization of dependencies and entities" html={DisplaCyLongHtml} height={450} />
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