August 30 to 01 2016, Santa Clara, USA.
Speaker "Thomas Levi" Details
Using NLP to categorize and find similar web pages
As the world leader in providing tools to create landing pages and drive conversions for marketing teams and agencies, Unbounce is constantly striving to give our customers the best information we can so they can make data-driven decisions to design and target their pages. As an initial step in that direction, we are looking for ways to automatically categorize and find similar pages among the half a million plus pages we currently host. In this talk, I will show how we can accomplish this by building a system that exploits techniques in natural language processing and topic modelling.
Thomas Levi started out with a doctorate in Theoretical Physics and String Theory from the University of Pennsylvania in 2006. His post-doctoral studies in cosmology and string theory, where he wrote 19 papers garnering 800+ citations, then took him to NYU and finally UBC. In 2012, he decided to move into industry, and took on the role of Senior Data Scientist at PlentyOfFish and then on to Director of Data Science at Unbounce in 2015. Thomas has been involved in diverse projects such as behaviour analysis, social network analysis, scam detection, Bot detection, matching algorithms, topic modelling and semantic analysis
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