Intent Classification in Question Answering I am trying to write a question answer intent classification program.

My task is given a set of unlabelled question and answers, I have
to write a program where I may group all the similar questions 
and identify their answers.

Once the answers for a group of similar questions are done, I have
to identify the intent or focus of answers. 

For example, if I have a set of questions like:

a) Q: where is Texas? A : It is in USA.
b) Q: where is California? A: It is in USA.
c) Q: where is NASA? A: It is in USA.
d) Q: who is Queen Elizabeth II? A: Queen of England.
e) Q: who is Donald Trump? A : President of USA.


Thus, 
I am trying to group questions a, b &amp; c as Location oriented question,
and d &amp; e as Official/Person oriented question. 

To solve this problem, 

I am trying to use a standard classifier, and as it identifies 'It is in USA' as the class,
I am trying to tag it tag it as "It/NA is/NA in/NA USA/LOC" to identify intent/focus 
of answer as Location. 

I am using a standard classifier like Naive Bayes and a standard Hidden Markov Model
based tagger. 

The result is more or less fine.
I am using two training set one for classifier and the other for tagging.

If any one of the esteemed scientists may kindly suggest how I am trying
to solve the problem?

Here, Q means question, A means answer. 

Apology for cross-posting
I don't know about the granularity of the intents. If they are just Person/Loc/Official, why not use question words(where,who,what etc.) to identify the intent? For example, "where" corresponds to "Loc", "Who" corresponds to "Person" etc. You can also use this as a feature in you classifier. 
Hope it helps!!