From fc5cd57869fe8b43f07919a4b9fffbaa670939e7 Mon Sep 17 00:00:00 2001
From: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Date: Tue, 31 Oct 2023 21:58:29 +0100
Subject: [PATCH] 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>
---
website/docs/usage/linguistic-features.mdx | 10 ++++++----
1 file changed, 6 insertions(+), 4 deletions(-)
diff --git a/website/docs/usage/linguistic-features.mdx b/website/docs/usage/linguistic-features.mdx
index 47259ce15..21cedd1ef 100644
--- a/website/docs/usage/linguistic-features.mdx
+++ b/website/docs/usage/linguistic-features.mdx
@@ -290,10 +290,7 @@ for token in doc:
| toward | `prep` | shift | `NOUN` | manufacturers |
| manufacturers | `pobj` | toward | `ADP` | |
-<ImageScrollable
- src="/images/displacy-long2.svg"
- width={1275}
-/>
+<ImageScrollable src="/images/displacy-long2.svg" width={1275} />
Because the syntactic relations form a tree, every word has **exactly one
head**. You can therefore iterate over the arcs in the tree by iterating over
@@ -720,6 +717,10 @@ identifier from a knowledge base (KB). You can create your own
[`KnowledgeBase`](/api/kb) and [train](/usage/training) a new
[`EntityLinker`](/api/entitylinker) using that custom knowledge base.
+As an example on how to define a KnowledgeBase and train an entity linker model,
+see [`this tutorial`](https://github.com/explosion/projects/blob/v3/tutorials/nel_emerson)
+using [spaCy projects](/usage/projects).
+
### Accessing entity identifiers {id="entity-linking-accessing",model="entity linking"}
The annotated KB identifier is accessible as either a hash value or as a string,
@@ -730,6 +731,7 @@ object, or the `ent_kb_id` and `ent_kb_id_` attributes of a
```python
import spacy
+# "my_custom_el_pipeline" is assumed to be a custom NLP pipeline that was trained and serialized to disk
nlp = spacy.load("my_custom_el_pipeline")
doc = nlp("Ada Lovelace was born in London")