Adds a worker to tag named entities using Stanford NER[1]

This code uses pyNER[2] to connect to a socket where Stanford NER is
listenning. For now, this means that the java process is assumed to be running
and this worker will fail if it's not.

[1] http://nlp.stanford.edu/software/CRF-NER.shtml
[2] https://github.com/dat/pyner
Sends text to StanfordNER encoded in UTF-8
Adjusts the Stanford NER server port according to changes in the deploy process[1]

[1] https://github.com/flavioamieiro/pypln-deploy/commit/7ce50b293d5ca4b604baff801e6ad3a7d79c2eb3
Fixes Stanford NER worker test
Uses both the 7 classes and the 3 classes classifier in the NER worker

Since the 3 classes classifier is more acurate, it is better to extract the
classes it recognizes. The rest of the classes are extracted using the 7
classes worker. One thing to note: this means that the 'O' class will have
entities that were recognized by the 7 classes classifier, but not the 3
classes.
Adapts 'get_requirements' in setup.py to ignore pip 'editable installs'
Changes the way the Stanford NER worker saves it's results

Saving data this way we keep positional information about each token, and it
will be easier to create visualizations for this data.
Adds script to start Stanford NER in development environment

This includes a script to download Stanford NER and extract it to the scripts
directory.
Adds Stanford_NER tests
Merge branch 'feature/stanford_ner' into develop