Lexical Graphs with Natural Language Processing using NLTK
Brian will talk about his experiences using Python and NLTK http://nltk.org/ to run language comparisons to generate lexical difference graphs like the one mentioned in the "Lexical Distance Among the Languages of Europe" article. http://bit.ly/1cS46Ba
The focus will be on the NLTK and how its internals work to process a language. This talk will be his best one ever.
Garbage Collection w/ Ref. Cycles
Reference counting is very useful but it has an odd problem. We employ a technique from graphs to approach it. The solution works but it's a bit slow.
There were 986 roadway fatalities in Illinois in 2013. Where's the data?
Seen on garish LED roadway signs all around Chicago on New Year's Eve, 2013: 986 TRAFFIC DEATHS IN 2013. It leads to many questions: On what roads? When did the accidents happen? What do we do now? I'm scared to drive. I will talk about purging my fears by finding the data to answer some of those questions. http://tothebeat.github.io/fatal-car-crashes/ This talk will involve PythonAnywhere, IPython, a module that's not even on PyPi (dbfpy), searching for and finding open government data, CartoDB, Google Fusion Tables, csv, and maybe Pandas. Rest assured, there will be no graphic photos.