Abstract: Methodologies for text analysis are improving, and deep learning, topic modeling and novel lexical parsing techniques are allowing practitioners to create powerful, useful and, importantly, interpretable models of language.
These techniques allow us to understand and summarize documents. In this talk, I'll discuss some of the problems facing traditional industries and how these solutions can be applied.
I'll start with my experience at the New York Times, where I built systems to help journalists cluster reader responses, find tips quicker, and find related articles. I'll focus on work I'm currently doing at NASA, where we are building systems to summarize literature and recommend datasets, collaborators and papers to new scientists.
Topics covered: Deep learning, Bayesian topic modeling, lexical parsing.
Perks: Live demo!
Bio: Alex has worked as a data scientist at The New York Times since July 2014. His work has primarily involved text modeling for newsroom, product and advertising stakeholders to create advanced recommendation engines, perform automated information retrieval for journalists and sell premium ads. His work has been written about or featured in The New York Times, The Wall Street Journal, on NPR, and in Columbia Journalism Review, and at conferences, and he has earned a Masters in Data Science and a Masters in Journalism from Columbia University.