NLP and Large-Scale Information Retrieval on Mathematical Texts.

We present a recommender system covering math and math physics papers from the arXiv, to assist researchers to quickly retrieve theorems and discover similar results from this vast corpus. The retrieval aims to discover not just syntactic, but also semantic similarity. We will discuss the challenges encountered and the experimental methodologies used.