title: Stanford typed dependencies using coreNLP in python
body: <p>In <a href="https://nlp.stanford.edu/software/dependencies_manual.pdf" rel="noreferrer">Stanford Dependency Manual</a> they mention  "Stanford typed dependencies" and particularly the type "neg" - negation  modifier. It is also available when using Stanford enhanced++ parser using the website. for example, the sentence: </p>
  
  <blockquote>
    <p>"Barack Obama was not born in Hawaii"</p>
  </blockquote>
  
  <p><a href="https://i.stack.imgur.com/dEo0q.png" rel="noreferrer"><img src="https://i.stack.imgur.com/dEo0q.png" alt="enter image description here"></a> </p>
  
  <p>The parser indeed find neg(born,not) </p>
  
  <p>but when I'm using the <code>stanfordnlp</code> python library, the only dependency parser I can get will parse the sentence as  follow:</p>
  
  <pre><code>('Barack', '5', 'nsubj:pass')
  
  ('Obama', '1', 'flat')
  
  ('was', '5', 'aux:pass')
  
  ('not', '5', 'advmod')
  
  ('born', '0', 'root')
  
  ('in', '7', 'case')
  
  ('Hawaii', '5', 'obl')
  </code></pre>
  
  <p>and the code that generates it: </p>
  
  <pre><code>import stanfordnlp
  stanfordnlp.download('en')  
  nlp = stanfordnlp.Pipeline()
  doc = nlp("Barack Obama was not born in Hawaii")
  a  = doc.sentences[0]
  a.print_dependencies()
  </code></pre>
  
  <p>Is there a way to get similar results to the enhanced dependency parser or any other Stanford parser that result in typed dependencies that will give me the negation modifier?</p>