diff --git a/book/ch05.rst b/book/ch05.rst
index 4a25d6ac..8d50d440 100644
--- a/book/ch05.rst
+++ b/book/ch05.rst
@@ -316,7 +316,7 @@ category of the Brown corpus:
 We can use these tags to do powerful searches using a graphical
 POS-concordance tool ``nltk.app.concordance()``.  Use it
 to search for any combination of words and POS tags, e.g.
-``N N N N``, ``hit/VD``, ``hit/VN``, or ``the ADJ man``.
+``NOUN NOUN NOUN NOUN``, ``hit/VBD``, ``hit/VBN``, or ``the ADJ man``.
 
 .. Screenshot
 
@@ -416,8 +416,9 @@ will do this for the WSJ tagset rather than the universal tagset:
 
 To clarify the distinction between ``VBD`` (past tense) and ``VBN``
 (past participle), let's find words which can be both ``VBD`` and
-``VBN``, and see some surrounding text:
+``VBN`` from the WSJ tagset, and see some surrounding text:
 
+    >>> cfd1 = nltk.ConditionalFreqDist(wsj) 
     >>> [w for w in cfd1.conditions() if 'VBD' in cfd1[w] and 'VBN' in cfd1[w]]
     ['Asked', 'accelerated', 'accepted', 'accused', 'acquired', 'added', 'adopted', ...]
     >>> idx1 = wsj.index(('kicked', 'VBD'))
@@ -565,7 +566,7 @@ the distinctions between the tags.
     >>> data = nltk.ConditionalFreqDist((word.lower(), tag)
     ...                                 for (word, tag) in brown_news_tagged)
     >>> for word in sorted(data.conditions()):
-    ...     if len(data[word]) > 3:
+    ...     if len(data[word]) >= 3:
     ...         tags = [tag for (tag, _) in data[word].most_common()]
     ...         print(word, ' '.join(tags))
     ...