Therefore, we can use S L to learn a projection for the source domain S prj where the separation between an instance x ∈ S L and its opposite-labelled nearest neighbours is maximised.
for an instance x we represent the set of k of its nearest neighbours NN(x, D, k) selected from a set D by a vector φ(x, D, k) as the sum of the word embeddings of the neighbours given by (1).