Q:

Word2Vec and Gensim parameters equivalence

Gensim is an optimized python port of Word2Vec (see http://radimrehurek.com/2013/09/deep-learning-with-word2vec-and-gensim/)
I am currently using these vectors: http://clic.cimec.unitn.it/composes/semantic-vectors.html
I am going to rerun the model training with gensim because there was some noisy tokens in their models. So i would like to find out what are some equivalent parameters for word2vec in gensim
And the parameters they used from word2vec are:

2-word context window, PMI weighting, no compression, 300K dimensions

What is the gensim equivalence when i train a Word2Vec model?
Is it:
>>> model = Word2Vec(sentences, size=300000, window=2, min_count=5, workers=4)

Is there a PMI weight option in gensim?
What is the default min_count used in word2vec?
There's another set of parameters from word2vec as such:

5-word context window, 10 negative samples, subsampling, 400 dimensions.

Is there a negative samples parameter in gensim?
What is the parameter equivalence of subsampling in gensim?

A:

The paper you link to compares word embeddings from a number of schemes, including Continuous Bag of Words (CBOW). CBOW is one of the models implemented in Gensim's "word2vec" model. The paper also discusses word embeddings obtained from Singular Value Decomposition with various weighting schemes, some involving PMI. There is no equivalence between SVD and word2vec, but if you want to do an SVD in gensim, it's called "LSA" or "Latent Semantic Analysis" when done in natural language processing.
The min_count parameter is set to 5 by default, as can be seen here. 
Negative Sampling and Hierarchical Softmax are two approximate inference methods for estimating a probability distribution over a discrete space (used when a normal softmax is too computationally expensive). Gensim's word2vec implements both. It uses hierarchical softmax by default, but you can use negative sampling by setting the hyperparameter negative to be greater than zero. This is documented in comments in gensim's code here as well. 

