Added some scaladoc in app.nlp.
Merge and fix conflict in app/nlp/phrase/Phrase.scala.
Remove buggy l2 regularized conjugate gradient (use trait if you want l2)
Added objects for pre-trained models

This commit includes four companion objects that are initialized
with the models pre-trained on the Microsoft Research Asia, Academia
Sinica, Peiking University and City University of Hong Kong corpuses
remove reference to nonexistent nlp.mention package
hopefully really fix build this time
Fix bug in scaled hinge loss and add smoothed hinge loss with adjustable margin/costs
Merge pull request #121 from oskarsinger/master

Added objects for pre-trained models
Added F1 score method and tweaked punct recognizer
Added mains to trained objects for testing
Deleted 2 trained objects and cleaned

Deleted the PKU and AS companion objects due to low F1 scores
and cleaned up the rest of the code a little
Merge pull request #123 from oskarsinger/master

Cleaned up some bad/unnecessary stuff
added classify cmd option

added classify cmd option; so that the `classify` examples from [tutorial](http://factorie.cs.umass.edu/tutorials/UsersGuide01Introduction.scala.html) could be executed, e.g.:

```
$ bin/fac classify --read-text-dirs sportsdir politicsdir --write-classifier mymodel.factorie
```
Merge pull request #125 from neil-rubens/patch-1

added classify cmd option
added option to tokenize venues because this gets better results on rexa data
Fixed LDAUtils to return proportions instead of counts when inferring topics for papers. Author coreference has returned to its usual performance.
Merge pull request #126 from michaelwick/merge-change

added option to tokenize venues because this gets better results on rexa...
added embeddings code
replace domain hash maps with java hashmaps since those are mysteriously much faster
added embeddings code