We use SGNS, CBOW and GloVe word embedding learning algorithms to learn 300 dimensional word embeddings from the Toronto books corpus.
likewise, for a randomly selected sentence S j / ∈ {S i−1 , S i , S i+1 }, the expect similarity between S j and S i would be low.