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 & c as Location oriented question, and d & 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