title: Incorrect (?) lemma returned by NLTK WordNetLemmatizer for particular words
body: <p>I was reading this <a href="https://towardsdatascience.com/building-a-text-normalizer-using-nltk-ft-pos-tagger-e713e611db8" rel="nofollow noreferrer">article</a> and experimenting on my own data, I found both the examples given in the article and one of my words didn't work as described.
  You can refer to the article for more information, though the question here has everything to get going.</p>
  <pre><code># stemmed root words: Books, Braveri, Harri, Transpar
  from nltk.stem.wordnet import WordNetLemmatizer as Lemmatizer
  
  # the article shared the same lemmatizer initialization.
  lem = WordNetLemmatizer()
  
  # returned 'harry' in the example without pos tag
  In [269]: lem.lemmatize('harri', pos='n')
  Out[269]: 'harri'
  
  In [270]: lem.lemmatize(&quot;Books&quot;, pos='n')
  Out[269]: 'Books'
  
  # returned 'book' in the example with pos tag
  In [270]: lem.lemmatize(&quot;Books&quot;, pos='v')
  Out[269]: 'Books'
  
  # my example root word, didn't change at all
  [ins] In [278]: lem.lemmatize(&quot;Transpar&quot;, pos=&quot;a&quot;)
  Out[278]: 'Transpar'
  [ins] In [281]: lem.lemmatize(&quot;Transpar&quot;, pos=&quot;n&quot;)
  Out[281]: 'Transpar'
  
  # returned 'bravery' in the example without pos tag
  [ins] In [280]: lem.lemmatize(&quot;Braveri&quot;, pos=&quot;n&quot;)
  Out[280]: 'Braveri'
  </code></pre>
  <p>the default pos tag for this lemmatizer is just <code>wordnet.NOUN</code> therefore providing pos tag or not wouldn't make a difference.
  FYI, <code>transpar</code> was originally <code>transparent</code>.</p>
  <p>The only difference was that the author stemmed the words using NLTK stemmer whereas I was using <code>texthero.stem</code>.</p>
  <p>Is it that I did it wrong or something has changed in NLTK?</p>