I am trying to make a simple chatbot for Frequently Asked Questions about any particular thing, for example some college admission. Most of the questions are unique and have no relation to each other. On being fed such questions and answers the bot tries to match the closest question. Currently I am using topic modelling( tfidf followed by lsa) with only the nouns and adj. in the input sentences. The topic value is set high. It is not very good in terms of accuracy.
I also tried parsing the sentence and finding out the root verb and its direct objects and its modifiers (using a dependancy parser) and then put a weight on each of them depending on their type, following which I did a straight match with the lemmatized input senteces and come up with a ratio based on the degree of overlapping words and their weights. But it was not scaling with a larger dataset.
Because there only 1 or 2 sentences about a topic I haven’t used classifiers.
Is there any one who has tried to solve a similar problem or someone knows some different approach to tackle this problem.
thanks