  Question Answering System (QAS) is used for information retrieval and natural
language processing (NLP) to reduce human effort. There are numerous QAS based
on the user documents present today, but they all are limited to providing
objective answers and process simple questions only. Complex questions cannot
be answered by the existing QAS, as they require interpretation of the current
and old data as well as the question asked by the user. The above limitations
can be overcome by using deep cases and neural network. Hence we propose a
modified QAS in which we create a deep artificial neural network with
associative memory from text documents. The modified QAS processes the contents
of the text document provided to it and find the answer to even complex
questions in the documents.
