title: StanfordNLP lemmatization cannot handle -ing words
body: <p>I've been experimenting with Stanford NLP toolkit and its lemmatization capabilities. I am surprised how it lemmatize some words. For example:</p>
  
  <pre><code>depressing -&gt; depressing
  depressed -&gt; depressed
  depresses -&gt; depress
  </code></pre>
  
  <p>It is not able to transform <code>depressing</code> and <code>depressed</code> into the same lemma. Simmilar happens with <code>confusing</code> and <code>confused</code>, <code>hopelessly</code> and <code>hopeless</code>. I am getting the feeling that the only thing it is able to do is remove the <code>s</code> if the word is in such form (e.g. <code>feels -&gt; feel</code>). Is such behaviour normal for Lematizatiors in English? I would expect that they would be able to transform such variations of common words into a same lemma.</p>
  
  <p>If this is normal, should I rather use stemmers? And, is there a way to use stemmers like Porter (Snowball, etc.) in StanfordNLP? There is no mention of stemmers in their documentation; however, there are some <code>CoreAnnotations.StemAnnotation</code> in the API. If not possible with StanfordNLP which stemmers do you recommend for use in Java?</p>