Plot words after reducing dimension of vectors

This is to see the relationship between Paris and France, Beijing and China,
sit and sitting, stand and standing, etc, in a 2D plane. Dimensionality
reduction was achieved through principle component analysis.
Train word vectors using gensim & BioScope
Get word2vec to give good results with gutenberg

Gutenberg is a source for free text database
Minor rename of file to reflect future change

try_gensim actually tries word2vec. We change the filename to try_word2vec
since we're probably going to have another script called try_doc2vec. This
makes the contrast between scripts clearer.
Get word2vec to take in a generator, not a list

Previously we had to get word2vec to take in a list of sentences, which
is expensive since all sentences have to be stored in memory before training.
We create a generator object that word2vec is happy to take in, so now training
is less memory intensive.
Prettify print statements in word2vec script
Fix: correct documentation of GutenbergSentences
Simple inputs to try out doc2vec

Inputs to doc2vec are a list of LabeledSentence. A LabeledSentence
consists of a list of words (like a 'sentence' in doc2vec) and a list of
labels. For our purposes it could well be a list of a single label, e.g.
LabeledSentence(['the', 'cat;], ['label1'])
Learn word vectors from BioScope data
Train word vectors from all BioScope data
Include where we got our data from, in README