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Update v2 docs [ci skip]

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Ines Montani 2019-09-28 15:57:22 +02:00
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9
website/docs/usage/spacy-101.md
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@ -238,15 +238,6 @@ of a model, see the usage guides on
</Infobox>
<Infobox title="📖 Entity Linking">
To learn more about entity linking in spaCy, and how to **train and update** the
entity linker predictions, see the usage guides on
[entity linking](/usage/linguistic-features#entity-linking) and
[training the entity linker](/usage/training#entity-linker).
</Infobox>
### Word vectors and similarity {#vectors-similarity model="vectors"}
import Vectors101 from 'usage/101/\_vectors-similarity.md'

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website/docs/usage/v2-2.md
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@ -11,10 +11,10 @@ menu:
spaCy v2.2 features improved statistical models, new pretrained models for
Norwegian and Lithuanian, better Dutch NER, as well as a new mechanism for
storing language data that makes the installation about **15&times; smaller** on
disk. We've also added a new API for **entity linking**, a new class to
efficiently **serialize annotations**, an improved and 10&times; faster phrase
matching engine, built-in scoring and **CLI training for text classification**
and a new command to analyze and **debug training data**. For the full
disk. We've also added a new class to efficiently **serialize annotations**, an
improved and **10&times; faster** phrase matching engine, built-in scoring and
**CLI training for text classification**, a new command to analyze and **debug
training data**, data augmentation during training and more. For the full
changelog, see the
[release notes on GitHub](https://github.com/explosion/spaCy/releases/tag/v2.2.0).
@ -45,36 +45,6 @@ overall. We've also added new core models for [Norwegian](/models/nb) (MIT) and
</Infobox>
### Entity linking API {#entity-linking}
> #### Example
>
> ```python
> nlp = spacy.load("my_custom_wikidata_model")
> doc = nlp("Ada Lovelace was born in London")
> print([(e.text, e.label_, e.kb_id_) for e in doc.ents])
> # [('Ada Lovelace', 'PERSON', 'Q7259'), ('London', 'GPE', 'Q84')]
> ```
Entity linking lets you ground named entities into the "real world". We're
excited to now provide a built-in API for training entity linking models and
resolving textual entities to unique identifiers from a knowledge base. The
annotated KB identifier is accessible as either a hash value or as a string from
a `Span` or `Token` object. For more details on entity linking in spaCy, check
out
[Sofie's talk](https://www.youtube.com/watch?v=PW3RJM8tDGo&list=PLBmcuObd5An4UC6jvK_-eSl6jCvP1gwXc&index=6)
at spaCy IRL 2019.
<Infobox>
**API:** [`EntityLinker`](/api/entitylinker),
[`KnowledgeBase`](/api/knowledgebase) **Code: **
[`bin/wiki_entity_linking`](https://github.com/explosion/spaCy/tree/master/bin/wiki_entity_linking)
**Usage: ** [Entity linking](/usage/linguistic-features#entity-linking),
[Training an entity linking model](/usage/training#entity-linker)
</Infobox>
### Serializable lookup table and dictionary API {#lookups}
> #### Example