One question that often comes to my mind is how does geographical location (and the culture that comes with it) influence the band’s music and therefore my taste as well. So that’s why I decided to use this data to create a cartogram of my listening data.
Crazy long title, I know, but it's not as complicated as it sounds. For my Natural Language Processing class we recently approached the problem of Word Sense Disambiguation from a Machine Learning perspective (we also looked at others, including lookup approaches like Lesk), using the simple, but effective, decision list algorithm. This is my attempt at visualizing the results.
A joint post by Brendan Whitfield & Will Paul
Source code is often the most up to date documentation available, but while code is written in a linear fashion, the execution is a network of calls that often jump across files. Known as a function call graph, this data can help programmers quickly grasp the structure of a program. How can we use visualization to make the process of grokking source code easier?
One of the first things you realize when working with any sort of linguistic data is just how ambiguous just about anything we say or write really is. From the smallest units of sound (what was that garbled bit of sound?) to the document unit of meaning (what was that article about?), there aren't many parts of language that we can be 100% sure of even as native speakers, much less as an outside observer, like our programs.
∣ Physical Computing Business and Legal Arduino
What if you could order drink with your coaster? This blog post explains how we made one using an Arduino and Node.js.