Therefore, PLSA finds a topic distribution for each concept definition that maximizes the log likelihood of the corpus X (LDA has a similar form): In this formulation, missing words do not contribute to the estimation of sentence semantics, i.e., excluding missing words (X ij = 0) in equation 1 does not make a difference.
empirical results show that given a small number of observed words, usually topic models can only find one topic (most evident topic) for a sentence, e.g., the concept definitions of bank#n#1 and stock#n#1 are assigned the financial topic only without any further discernability.