Reading papers

I've been pretty much dry on ideas of how to improve the model I described in the previous post so under the advice of the Ph.D. student I'm working with I've been reading papers that pertain to the same problem. Here are the main ones:

Large-scale Semantic Parsing via Schema Matching and Lexicon Extension: http://cis-linux1.temple.edu/~yates/papers/textual-schema-matching.pdf

Semantic Parsing via Paraphrasing: http://cs.stanford.edu/~pliang/papers/paraphrasing-acl2014.pdf

Semantic Parsing on Freebase from Question-Answer Pairs: http://cs.stanford.edu/~pliang/papers/freebase-emnlp2013.pdf

Enhancing Freebase Question Answering Using Textual Evidence (very recent): http://arxiv.org/abs/1603.00957

Other than that, there's really not much to talk about about the past week. I did spend a lot of time learning about machine learning (partly out of curiosity, but also because I may need it later in this project), specifically artificial neural networks (ANNs). On the surface, the idea sounds simple but I found the details difficult to wrap my mind around even after reading quite a few introductory articles on them, including the Wikipedia page. The one that made it actually "click" was this one.