Question answering is an amazing problem. I built factoid QA systems using a mix of NLP, open relation extraction (dependency relation classification via SVMs),

Question answering is an amazing problem. I built factoid QA systems using a mix of NLP, open relation extraction (dependency relation classification via SVMs), semantic web stuff via triple stores (virtuoso) and answer candidate ranking algorithms (classify the questions in to different types via keywords) and using those to determine whether I should use a search engine, or lookup system. I think for those who want to get started in the field understanding some of the more complex NLP techniques, information retrieval, and some idea of how triple stores work gets you to building a QA system. This is actually why I jumped in to deep learning. The hardest part of NLP for open ended question answering is coming up with all of the different features. Deep Learning with textual representations fixes this. The tech stack was http://uima.apache.org http://uima.apache.org for those who are curious. I'd be happy to answer anything specific. QA systems are a little esoteric due to the combination of techniques used to build them, but they aren't as mysterious if you put in the effort to understand what they actually are.