Distributed Graph Representations Using the Mueller Report

Node2Vec in 14 lines of code

Note: This is part 2 of my series on the Mueller Report. Please take a look at Part 1 if you are interested in how I built the graph. One thing that would be useful when navigating a document (or set of documents) like the Mueller Report is the ability to find things that are ‘like’ other things. For example, if you are trying to follow the thread of a story through the document, you might want to find all the paragraphs that are about similar things to the paragraph you are interested in. [Read More]

Graph Visualization of The Mueller Report With SpaCy and PyVis

Building a contextual paragraph recomendation engine

One of the most interesting talks I heard at Strata in San Francisco this year was “Towards deep and representation learning for talent search at LinkedIn”. In the talk, Gungor explained how he took advantage of LinkedIn’s economic graph to build a hyper-personalized search engine. Ever since then I’ve had graphs firmly planted in my mind. Not these graphs: Graph More like these: Network Specifically, I’ve been trying to understand how graph network techniques can be applied in various domains, including natural language processing. [Read More]

Monte Carlo Simulation For Childrens Stories

Using web scraping, SpaCy, and monte carlo simulation to check children story logic

Bedtime has historically been a battle for our family. Kids have this impressive ability to fall asleep when you want them awake and vigorously stay awake when you want them to sleep. To combat the insanity, we read a few books every night before bed. There is a great series of books for young children called “The Magic Tree House”. The series follows two children (Jack and Annie) who transport to other times and places for some mystery or adventure. [Read More]