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Fixed tags for the graphical condordance #252

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9b696ad
Fixed tags for the graphical condordance
MrSampson Oct 6, 2021
41993c2
Fixed the CFD query
MrSampson Oct 7, 2021
e8ab22c
Fixed comparison
MrSampson Oct 7, 2021
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7 changes: 4 additions & 3 deletions book/ch05.rst
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Expand Up @@ -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

Expand Down Expand Up @@ -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'))
Expand Down Expand Up @@ -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))
...
Expand Down