explanatory power of working examples

The NLP algorithms I've been studying since I started back at school aren't particularly complex. But they're often described with really dense notation: maybe your field does this too! Here's a description, for example, of how to calculate an "outside probability" -- it's the (joint) probability that a particular nonterminal symbol covers a certain chunk of text, and the words outside the span of that nonterminal. This is from Fei Xia's lecture slides (and I think these are pretty good).



Maybe what I need is more practice picking apart dense notation, but in all honesty I have trouble keeping track of what the different letters mean. Maybe a nice dynamic programming implementation springs to mind for people smarter than me, but I have to stare at it (and the surrounding slides) for quite a while!

I think I'd be making a pretty good contribution to the world if I took the algorithms I'm learning and wrote down the most straightforward pseudocode and prose versions I can, with a running Python implementation and descriptive variable names. Surely many people out there would find code easier to digest!

Somebody's already done precisely this with the Viterbi Algorithm wikipedia page, and I'm very grateful to that somebody.