# Approaches for implementing Domain specific Question answering System

Given several wikipedia articles on different movies. What are the different approaches to implement a QA system to answer different quires related to movies.

Dataset : Wikipedia articles

Input   : natural language query, eg : who directed terminator ?

Output  : The Terminator is a 1984 American science fiction action film directed by James Cameron


Different approaches available are :

1 IR Based approaches

In IR based approaches the similarities between query and passaeges are considered and best suitable passages are selected as answer (as Google does)

2 Ontology based

In Ontology based prior domain knowledge is required about the domain. And documents need to be mapped accordingly.

3 Machine learning based

In ML based approaches large training set of documents is needed for training.

What are the other cons and pros of each approaches?

Which of these approaches suits the best for this use case ? Also is there any other methods ?

• Mind adding more details? This is a bit broad (and/or) unclear atm :) – Dawny33 Jun 17 '16 at 9:54
• I think this is comparable to visual question answering (VQA), where you pose a (textual) question about an image and the model gives an answer. Sreejithc321 would like to do that but with wikipedia articles about movies instead of images. See github.com/abhshkdz/neural-vqa or iamaaditya.github.io/2016/04/… for examples. Neural networks can be used for that - but you'll need LOTS of questions & answers & movie articles for training. – stmax Jun 17 '16 at 10:39
• @Dawny33 Please check now – Sreejithc321 Jun 20 '16 at 5:39
• @Sreejithc321 Looks fine now. Thanks for making it clear. [Voted to reopen] :) – Dawny33 Jun 20 '16 at 5:43
• I have voted to re-open, but I still think it could do with some refining to make it less broad. I suggest drop the implementation question, and ask it separately once you have made an informed decision about the approach (with choice of approach made and some specific examples of data and goals). – Neil Slater Jun 20 '16 at 6:56

While the actual "best system" depends heavily on a number of factors including your goals and your resources, it is possible to discuss some general pros and cons to each system.