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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.
63 lines
3.1 KiB
Markdown
63 lines
3.1 KiB
Markdown
After tokenization, spaCy can **parse** and **tag** a given `Doc`. This is where
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the statistical model comes in, which enables spaCy to **make a prediction** of
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which tag or label most likely applies in this context. A model consists of
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binary data and is produced by showing a system enough examples for it to make
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predictions that generalize across the language – for example, a word following
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"the" in English is most likely a noun.
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Linguistic annotations are available as
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[`Token` attributes](/api/token#attributes). Like many NLP libraries, spaCy
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**encodes all strings to hash values** to reduce memory usage and improve
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efficiency. So to get the readable string representation of an attribute, we
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need to add an underscore `_` to its name:
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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 token in doc:
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print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
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token.shape_, token.is_alpha, token.is_stop)
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```
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> - **Text:** The original word text.
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> - **Lemma:** The base form of the word.
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> - **POS:** The simple part-of-speech tag.
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> - **Tag:** The detailed part-of-speech tag.
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> - **Dep:** Syntactic dependency, i.e. the relation between tokens.
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> - **Shape:** The word shape – capitalization, punctuation, digits.
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> - **is alpha:** Is the token an alpha character?
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> - **is stop:** Is the token part of a stop list, i.e. the most common words of
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> the language?
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| Text | Lemma | POS | Tag | Dep | Shape | alpha | stop |
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| ------- | ------- | ------- | ----- | ---------- | ------- | ------- | ------- |
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| Apple | apple | `PROPN` | `NNP` | `nsubj` | `Xxxxx` | `True` | `False` |
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| is | be | `VERB` | `VBZ` | `aux` | `xx` | `True` | `True` |
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| looking | look | `VERB` | `VBG` | `ROOT` | `xxxx` | `True` | `False` |
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| at | at | `ADP` | `IN` | `prep` | `xx` | `True` | `True` |
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| buying | buy | `VERB` | `VBG` | `pcomp` | `xxxx` | `True` | `False` |
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| U.K. | u.k. | `PROPN` | `NNP` | `compound` | `X.X.` | `False` | `False` |
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| startup | startup | `NOUN` | `NN` | `dobj` | `xxxx` | `True` | `False` |
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| for | for | `ADP` | `IN` | `prep` | `xxx` | `True` | `True` |
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| \$ | \$ | `SYM` | `$` | `quantmod` | `$` | `False` | `False` |
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| 1 | 1 | `NUM` | `CD` | `compound` | `d` | `False` | `False` |
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| billion | billion | `NUM` | `CD` | `probj` | `xxxx` | `True` | `False` |
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> #### Tip: Understanding tags and labels
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>
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> Most of the tags and labels look pretty abstract, and they vary between
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> languages. `spacy.explain` will show you a short description – for example,
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> `spacy.explain("VBZ")` returns "verb, 3rd person singular present".
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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 dependencies 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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