Will/optimize ease

* Cache the processed text (tokenized / tagged / stemmed) so it can be re-used for all criteria in a rubric.
* Add a script to benchmark EASE.

This results in about a 63% performance improvement on the benchmark (4.4 seconds down to 1.5 in my VM).

@stephensanchez Please review, but not until after the hackathon :)
Yeah, that's what I ended up doing in the will/classy branch.  I'm working on merging those changes back into AI grading.