README.md: Design Considerations -> Development Notes

Later, we will split this to a different file.
doc/HIGHLEVEL.md: Passage and answer ranking to be machine learned
Add first draft of ML documentation
Revamp passage scoring: Introduced PassageFeature and PassScoreSimple

Rather than directly assigning the score in PassByClue, a bunch of
features is stored in the CAS and a separate step converts them to
score.  This step will be a machine learning model in the future,
but right now it's just the same equation we used up to now.

(Except that we lose a sqrt(), but that doesn't change the eventual
ordering, obviously.)
GoldStandardAnswerPrinter.output(): Take QuestionInfo directly
PassGSHook: New passextract consumer, dump training data; include passextract stats in GoldStandard output
PassGSHook: Fix run with the interactive frontend
PassByClue: Fix matching clues with regex-special meta characters
PassScoreSimple: We must first unindex passage, then set score, otherwise the index is messed up
PassGSHook: Do away without the superfluous newlines in mltraining
PassGSHook: Write class as 1/0 instead of true/false
Merge branch 'master' into mlscoring

Conflicts:
	README.md
PassGSHook: Separate set of passages within each document with an empty line
Merge branch 'master' into mlscoring
Merge branch 'master' into mlscoring
data/eval/curated-measure.sh: In train mode, enable mltraining
Merge branch 'master' into mlscoring

Conflicts:
	data/eval/curated-measure.sh
doc/ML.md -> data/ml/README.md
data/ml/passextract-train.py: New script, document experiments with passextract logit classifier
data/ml/passextract-train.py: Fix test_portion value to be in sync with specs