diff --git a/website/docs/usage/linguistic-features.md b/website/docs/usage/linguistic-features.md
index 42476cd98..f8f47ab53 100644
--- a/website/docs/usage/linguistic-features.md
+++ b/website/docs/usage/linguistic-features.md
@@ -512,7 +512,7 @@ nlp = spacy.load("en_core_web_sm", disable=["parser"])
 
 spaCy features an extremely fast statistical entity recognition system, that
 assigns labels to contiguous spans of tokens. The default
-[trained pipelines](/models) can indentify a variety of named and numeric
+[trained pipelines](/models) can identify a variety of named and numeric
 entities, including companies, locations, organizations and products. You can
 add arbitrary classes to the entity recognition system, and update the model
 with new examples.
@@ -550,7 +550,7 @@ on a token, it will return an empty string.
 > - `I` – Token is **inside** a multi-token entity.
 > - `L` – Token is the **last** token of a multi-token entity.
 > - `U` – Token is a single-token **unit** entity.
-> - `O` – Toke is **outside** an entity.
+> - `O` – Token is **outside** an entity.
 
 ```python
 ### {executable="true"}
@@ -1498,7 +1498,7 @@ that time, the `Doc` will already be tokenized.
 
 This process of splitting a token requires more settings, because you need to
 specify the text of the individual tokens, optional per-token attributes and how
-the should be attached to the existing syntax tree. This can be done by
+the tokens should be attached to the existing syntax tree. This can be done by
 supplying a list of `heads` – either the token to attach the newly split token
 to, or a `(token, subtoken)` tuple if the newly split token should be attached
 to another subtoken. In this case, "New" should be attached to "York" (the