  Question-answering (QA) is certainly the best known and probably also one of
the most complex problem within Natural Language Processing (NLP) and
artificial intelligence (AI). Since the complete solution to the problem of
finding a generic answer still seems far away, the wisest thing to do is to
break down the problem by solving single simpler parts. Assuming a modular
approach to the problem, we confine our research to intent classification for
an answer, given a question. Through the use of an LSTM network, we show how
this type of classification can be approached effectively and efficiently, and
how it can be properly used within a basic prototype responder.
