Our objective is to make word v j be learned by topics in document d i , while not be learned by topics in the negative sampled document d .
we only take word v j in V into consideration during the learning procedure, which can be implemented as dot-multiplying p(V|d i ) with the indicator vector of Particularly, the objective is to make the predicted conditional probabilityp(v j |d i ) approach the observed conditional probability p(v j |d i ) (i.e., term frequency of word v j in document d i ), while make the conditional probabilityp( ) approach zero.