doc/HIGHLEVEL.md: Theory docs on the extra Evidence Gathering pipeline stage
Merge branch 'master' into evidence
doc/HIGHLEVEL: Clarify the 'Answer Analysis' phase description
YodaQA pipeline: Unroll createAnswerMergeAndScoreDescription()

We will want to add some flow control in the middle of that.
YodaQA pipeline: Add support for AnswerHitlist (de)serialization

We can now save the AnswerHitlist before final scoring, and restart
the pipeline only for re-scoring.  This means we do not have to
rerun the whole process when we re-train AnswerScore* but just
re-run the scoring.  (The scripts still need to be tweaked for this.)

(While this is pretty nice in itself, this is going to be quite
essential when evidence gathering and second phase scoring is
implemented.  We will have two serialization points then.)
AnswerScoreLogistic: Move model to an external resource file, tweak the format

We will update this file from an external script automagically.
AnswerScoreLogistic.loadModel(): Factor out code from initialize()
data/eval/train-and-eval.sh: New script to supersede curated-measure.sh

We now have a new method of combined benchmarking that takes advantage
of the answers.xmi save/load pipeline functionality. We can now
benchmark with re-trained model with a minimal overhead.
AnswerHitlistCAS: Split to Question, AnswerHitlist views and carry full question data

This will be useful when we unravel the hitlist back to do further analysis of some answers.
YodaQA pipeline: Add an extra 'Evidence Gathering' phase, so far no-op

After scoring once, we run (new CAS multiplier) AnswerSplitter to create
CandidateAnswerCASes for top 5 answers, then do... nothing yet, merge
them back (reusing AnswerMerger, modified to carry the hitlist along),
optionally snapshot and re-score.
YodaQA: - stray debug print
AnswerScor*: Differentiate between scoring phases

With this change, we can finally train and use different models for
different scoring phases.

AnswerScoreLogistic1.model is just a copy of the first model.
AnswerScoreLogistic: Add a debug print marking each process()
YodaQA pipeline: Add an extra 'Hitlist Pruning' phase, keeping just the top 100 answers

We cut off only 13/430 answers from the recall, and aim to reduce the noise.
Add some debug prints related to pipeline skips and serialization
AnswerHitlistDeserialize: Grab QuestionInfo from the direct jcas, which is a QuestionCAS after all
YodaQA: Fix several control flow mistakes for late load cases
data/ml/answer-train.py: Fix crashes when testing on a tiny sample
data/eval/train-and-eval.sh: Add support for training on serialization points #1 and #2 too
data/eval/train-and-eval.sh: Remove some stale debug stuff