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Clarify EL example in docs (#13071)
* add comment that pipeline is a custom one * add link to NEL tutorial * prettier * revert prettier reformat * revert prettier reformat (2) * fix typo Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com> --------- Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com>
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@ -290,10 +290,7 @@ for token in doc:
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| toward | `prep` | shift | `NOUN` | manufacturers |
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| toward | `prep` | shift | `NOUN` | manufacturers |
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| manufacturers | `pobj` | toward | `ADP` | |
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| manufacturers | `pobj` | toward | `ADP` | |
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<ImageScrollable
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<ImageScrollable src="/images/displacy-long2.svg" width={1275} />
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src="/images/displacy-long2.svg"
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width={1275}
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/>
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Because the syntactic relations form a tree, every word has **exactly one
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Because the syntactic relations form a tree, every word has **exactly one
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head**. You can therefore iterate over the arcs in the tree by iterating over
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head**. You can therefore iterate over the arcs in the tree by iterating over
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@ -720,6 +717,10 @@ identifier from a knowledge base (KB). You can create your own
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[`KnowledgeBase`](/api/kb) and [train](/usage/training) a new
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[`KnowledgeBase`](/api/kb) and [train](/usage/training) a new
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[`EntityLinker`](/api/entitylinker) using that custom knowledge base.
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[`EntityLinker`](/api/entitylinker) using that custom knowledge base.
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As an example on how to define a KnowledgeBase and train an entity linker model,
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see [`this tutorial`](https://github.com/explosion/projects/blob/v3/tutorials/nel_emerson)
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using [spaCy projects](/usage/projects).
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### Accessing entity identifiers {id="entity-linking-accessing",model="entity linking"}
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### Accessing entity identifiers {id="entity-linking-accessing",model="entity linking"}
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The annotated KB identifier is accessible as either a hash value or as a string,
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The annotated KB identifier is accessible as either a hash value or as a string,
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@ -730,6 +731,7 @@ object, or the `ent_kb_id` and `ent_kb_id_` attributes of a
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```python
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```python
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import spacy
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import spacy
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# "my_custom_el_pipeline" is assumed to be a custom NLP pipeline that was trained and serialized to disk
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nlp = spacy.load("my_custom_el_pipeline")
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nlp = spacy.load("my_custom_el_pipeline")
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doc = nlp("Ada Lovelace was born in London")
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doc = nlp("Ada Lovelace was born in London")
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