title: Stemmers vs Lemmatizers
body: <p>Natural Language Processing (NLP), especially for English, has evolved into the stage where stemming would become an archaic technology if "perfect" lemmatizers exist. It's because stemmers change the surface form of a word/token into some meaningless stems. </p>
  
  <p>Then again the definition of the "perfect" lemmatizer is questionable because different NLP task would have required different level of lemmatization. E.g. <a href="https://stackoverflow.com/questions/14489309/convert-words-between-verb-noun-adjective-forms">Convert words between verb/noun/adjective forms</a>. </p>
  
  <p><strong>Stemmers</strong> </p>
  
  <pre><code>[in]: having
  [out]: hav
  </code></pre>
  
  <p><strong>Lemmatizers</strong></p>
  
  <pre><code>[in]: having
  [out]: have
  </code></pre>
  
  <ul>
  <li><p>So the question is, are English stemmers any useful at all today? Since we have a plethora of lemmatization tools for English</p></li>
  <li><p>If not, then how should we move on to build robust lemmatizers that
  can take on <code>nounify</code>, <code>verbify</code>, <code>adjectify</code> and <code>adverbify</code>
  preprocesses?</p></li>
  <li><p>How could the lemmatization task be easily scaled to other languages
  that have similar morphological structures as English?</p></li>
  </ul>