title: Finding meaningful sub-sentences from a sentence
body: <p>Is there a way to to find all the sub-sentences of a sentence that still are meaningful and contain at least one subject, verb, and a predicate/object?</p>
  
  <p>For example, if we have a sentence like "I am going to do a seminar on NLP at SXSW in Austin next month". We can extract the following meaningful sub-sentences from this sentence: "I am going to do a seminar", "I am going to do a seminar on NLP", "I am going to do a seminar on NLP at SXSW", "I am going to do a seminar at SXSW", "I am going to do a seminar in Austin", "I am going to do a seminar on NLP next month", etc.</p>
  
  <p>Please note that there is no deduced sentences here (e.g. "There will be a NLP seminar at SXSW next month". Although this is true, we don't need this as part of this problem.) . All generated sentences are strictly part of the given sentence.</p>
  
  <p>How can we approach solving this problem? I was thinking of creating annotated training data that has a set of legal sub-sentences for each sentence in the training data set. And then write some supervised learning algorithm(s) to generate a model.</p>
  
  <p>I am quite new to NLP and Machine Learning, so it would be great if you guys could suggest some ways to solve this problem.</p>