diff --git a/website/docs/usage/101/_pos-deps.md b/website/docs/usage/101/_pos-deps.md
index 1a438e424..1e8960edf 100644
--- a/website/docs/usage/101/_pos-deps.md
+++ b/website/docs/usage/101/_pos-deps.md
@@ -36,7 +36,7 @@ for token in doc:
 | Text    | Lemma   | POS     | Tag   | Dep        | Shape   | alpha   | stop    |
 | ------- | ------- | ------- | ----- | ---------- | ------- | ------- | ------- |
 | Apple   | apple   | `PROPN` | `NNP` | `nsubj`    | `Xxxxx` | `True`  | `False` |
-| is      | be      | `VERB`  | `VBZ` | `aux`      | `xx`    | `True`  | `True`  |
+| is      | be      | `AUX`   | `VBZ` | `aux`      | `xx`    | `True`  | `True`  |
 | looking | look    | `VERB`  | `VBG` | `ROOT`     | `xxxx`  | `True`  | `False` |
 | at      | at      | `ADP`   | `IN`  | `prep`     | `xx`    | `True`  | `True`  |
 | buying  | buy     | `VERB`  | `VBG` | `pcomp`    | `xxxx`  | `True`  | `False` |
diff --git a/website/docs/usage/adding-languages.md b/website/docs/usage/adding-languages.md
index d42aad705..29a9a1c27 100644
--- a/website/docs/usage/adding-languages.md
+++ b/website/docs/usage/adding-languages.md
@@ -662,7 +662,7 @@ One thing to keep in mind is that spaCy expects to train its models from **whole
 documents**, not just single sentences. If your corpus only contains single
 sentences, spaCy's models will never learn to expect multi-sentence documents,
 leading to low performance on real text. To mitigate this problem, you can use
-the `-N` argument to the `spacy convert` command, to merge some of the sentences
+the `-n` argument to the `spacy convert` command, to merge some of the sentences
 into longer pseudo-documents.
 
 ### Training the tagger and parser {#train-tagger-parser}
diff --git a/website/docs/usage/linguistic-features.md b/website/docs/usage/linguistic-features.md
index 84bb3d71b..9031a356f 100644
--- a/website/docs/usage/linguistic-features.md
+++ b/website/docs/usage/linguistic-features.md
@@ -471,7 +471,7 @@ doc = nlp.make_doc("London is a big city in the United Kingdom.")
 print("Before", doc.ents)  # []
 
 header = [ENT_IOB, ENT_TYPE]
-attr_array = numpy.zeros((len(doc), len(header)))
+attr_array = numpy.zeros((len(doc), len(header)), dtype="uint64")
 attr_array[0, 0] = 3  # B
 attr_array[0, 1] = doc.vocab.strings["GPE"]
 doc.from_array(header, attr_array)
@@ -1143,9 +1143,9 @@ from spacy.gold import align
 other_tokens = ["i", "listened", "to", "obama", "'", "s", "podcasts", "."]
 spacy_tokens = ["i", "listened", "to", "obama", "'s", "podcasts", "."]
 cost, a2b, b2a, a2b_multi, b2a_multi = align(other_tokens, spacy_tokens)
-print("Misaligned tokens:", cost)  # 2
+print("Edit distance:", cost)  # 3
 print("One-to-one mappings a -> b", a2b)  # array([0, 1, 2, 3, -1, -1, 5, 6])
-print("One-to-one mappings b -> a", b2a)  # array([0, 1, 2, 3, 5, 6, 7])
+print("One-to-one mappings b -> a", b2a)  # array([0, 1, 2, 3, -1, 6, 7])
 print("Many-to-one mappings a -> b", a2b_multi)  # {4: 4, 5: 4}
 print("Many-to-one mappings b-> a", b2a_multi)  # {}
 ```
@@ -1153,7 +1153,7 @@ print("Many-to-one mappings b-> a", b2a_multi)  # {}
 Here are some insights from the alignment information generated in the example
 above:
 
-- Two tokens are misaligned.
+- The edit distance (cost) is `3`: two deletions and one insertion.
 - The one-to-one mappings for the first four tokens are identical, which means
   they map to each other. This makes sense because they're also identical in the
   input: `"i"`, `"listened"`, `"to"` and `"obama"`.
diff --git a/website/docs/usage/rule-based-matching.md b/website/docs/usage/rule-based-matching.md
index 1db2405d1..f7866fe31 100644
--- a/website/docs/usage/rule-based-matching.md
+++ b/website/docs/usage/rule-based-matching.md
@@ -1158,17 +1158,17 @@ what you need for your application.
 > available corpus.
 
 For example, the corpus spaCy's [English models](/models/en) were trained on
-defines a `PERSON` entity as just the **person name**, without titles like "Mr"
-or "Dr". This makes sense, because it makes it easier to resolve the entity type
-back to a knowledge base. But what if your application needs the full names,
-_including_ the titles?
+defines a `PERSON` entity as just the **person name**, without titles like "Mr."
+or "Dr.". This makes sense, because it makes it easier to resolve the entity
+type back to a knowledge base. But what if your application needs the full
+names, _including_ the titles?
 
 ```python
 ### {executable="true"}
 import spacy
 
 nlp = spacy.load("en_core_web_sm")
-doc = nlp("Dr Alex Smith chaired first board meeting of Acme Corp Inc.")
+doc = nlp("Dr. Alex Smith chaired first board meeting of Acme Corp Inc.")
 print([(ent.text, ent.label_) for ent in doc.ents])
 ```
 
@@ -1233,7 +1233,7 @@ def expand_person_entities(doc):
 # Add the component after the named entity recognizer
 nlp.add_pipe(expand_person_entities, after='ner')
 
-doc = nlp("Dr Alex Smith chaired first board meeting of Acme Corp Inc.")
+doc = nlp("Dr. Alex Smith chaired first board meeting of Acme Corp Inc.")
 print([(ent.text, ent.label_) for ent in doc.ents])
 ```