Is there a way to find all the sub-sentences of a sentence that still are meaningful and contain at least one subject, verb, and a predicate/object?
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.
Please note that there are no deduced sentences here (e.g. "There will be an 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.
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.
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.