Option not to generate >1 instance per sentence

Previously for each speculative sentence in the BioScope data, we generated
more than one positive instance by considering the scope within the sentence.
We now add a command line argument to disable that generation, such that each
sentence in BioScope only generates one training instance.

This allows us to compare the performance of the same learning algorithm with
two differently generated labeled datasets.
Option to plot ROC of baseline algorithms
Evaluate doc2vec with training error of classifier

We previously stopped training the doc2vec model when a score that we designed
ourselves started to fall. Now at each epoch we train a logistic regressor
(with the features from doc2vec) and take the training error as a score.
Remove unnecessary code
Split data into train/test/CV

TODO
* Change scoring of logistic regressor from accuracy to MSE
Remove functionality for learning test epochs
Move functions around

There was a local function that we move out of the main clause so that we can
call the function from outside the script.
Train recognizer on labeled and unlabeled data

Sequence
- We split the labeled data into training and test set.
- We add unlabeled data to the training set.
- We train doc2vec with the training set.
  At each epoch we train a logistic classifier and measure its F1 score on the
  training data
  We stop training the doc2vec when this F1 score starts going down
- To predict a test sentence, we converge using an arbitrary small learning
  rate 0.001 and an arbitrary large number of epochs 50
Merge branch 'master' of https://github.com/boonjiashen/speculative-language-recognizer