doc/TODO.md Immediate: Update based on recent work

Using Wordnet sp instead of per-LAT binary value fails:
 test 6a56d55 2014-10-23 AnswerScoreLogistic:... 79/298/430 18.4%/69.3% avgscore 0.482 avgtime 32.156

Carrying it per-LAT in addition to the binary value overfits:
train 5b314ef 2014-10-23 AnswerScoreLogistic:... 98/302/430 22.8%/70.2% avgscore 0.491 avgtime 30.759
 test 5b314ef 2014-10-23 AnswerScoreLogistic:... 95/298/430 22.1%/69.3% avgscore 0.489 avgtime 31.128

-sqrt(-sp) or raw sp instead of exp(sp) is terrible:
 test 944cdd3 2014-10-23 AnswerScoreLogistic:... 76/298/430 17.7%/69.3% avgscore 0.480 avgtime 30.869
(raw sp had even worse training performance so I didn't even fully
benchmark it)

One-sided WN specificity makes actually no sense as essentially there is
almost no hypernyme expansion on the answer side, so we actually already
are one-sided.
ClueFocus -> ClueLAT, generate clues for all sp=0 LATs

This e.g. brings 'temperature' clue to 'how hot' query.

We needed however to drop the reliable flag as e.g. 'hot' also gets
converted to 'emotionality', which is not helpful.
AnswerScoreLogistic: Retrained with updated model
CluesMergeByText: Let mergedclue.reliable = any(clues.reliable)

Before, we would inherit the reliable property from the heaviest clue,
but that doesn't really make any sense.
CluesMergeByText subdueInfo(): Add debug info on reliability of merged clues
ClueByTokenConstituent: Mark CluePhrase clues as unreliable

This way, we avoid e.g. ClueSubject phrasal clues being marked reliable.
For proximity queries, all the tokens will seep into them anyway, and we
wouldn't include them in the fulltext search on their own anyway.
question.LATByFocus: Generate W*-LATs with sp<0.0

They are not as specific as they could be, and this will prevent
ClueLATs generated for these - something that typically indeed doesn't
make much sense.
question.LATByFocus: Change label from quantity -> measure, appropriate wordnet sense
AnswerScoreLogistic: Retrained with updated model
Merge branch 'master' into f/alat-nown