1. A computer controlled method for adding terms to a probabilistic latent semantic analysis (PLSA) model trained by applying an expectation maximization algorithm utilizing a set of expectation maximization equations corresponding to a PLSA model based on using P(z|w), the method comprising: identifying by a processor, at least one new term w from a document d to be added to said trained PLSA model; incrementally adding said at least one new term to said trained PLSA model, the incrementally adding step including applying said expectation maximization algorithm utilizing only a subset comprising at least one of said expectation maximization equations, wherein parameters dependent on the new term w and the document d are used in the incrementally adding step, the set of expectation maximization equations comprising: P ⁡ ( z ❘ d , w ) = P ⁡ ( z ❘ d ) ⁢ P ⁡ ( z ❘ w ) ⁢ / ⁢ P ⁡ ( z ) ∑ z ′ ⁢ ⁢ P ⁡ ( z ′ ❘ d ) ⁢ P ⁡ ( z ′ ❘ w ) ⁢ / ⁢ P ⁡ ( z ′ ) ; P ⁢ ( z ❘ w ) = ∑ d ⁢ ⁢ f ⁡ ( d , w ) ⁢ P ⁡ ( z ❘ d , w ) ∑ d , z ′ ⁢ ⁢ f ⁡ ( d , w ) ⁢ P ⁡ ( z ′ ❘ d , w ) ; ⁢ P ⁢ ( z ❘ d ) = ∑ w ⁢ ⁢ f ⁡ ( d , w ) ⁢ P ⁡ ( z ❘ d , w ) ∑ w , z ′ ⁢ ⁢ f ⁡ ( d , w ) ⁢ P ⁡ ( z ′ ❘ d , w ) ; ⁢ and ⁢ P ⁡ ( z ) = ⁢ ∑ d , w ⁢ ⁢ f ⁡ ( d , w ) ⁢ P ⁡ ( z ❘ d , w ) ∑ d , w ⁢ ⁢ f ⁡ ( d , w ) , wherein P(z) represents a probability of a latent class z, P(z|d) represents a probability of a latent class z given a document d, P(z|w) represents a probability of a latent class z given a term w, and f(d,w) represents the number of times the term w occurs in the document d; and wherein the incrementally adding said at least one new term further comprises: keeping track of a total count N, wherein: N adj = N old + ∑ d new ∈ D new ⁢ ⁢ ∑ w ∈ W old ⋃ W new ⁢ ⁢ f ⁡ ( d new , w ) ; P adj ⁡ ( z ) = N old ⁢ P old ⁡ ( z ) + ∑ d new ∈ D new ⁢ ∑ w ∈ W old ⋃ W new ⁢ ⁢ f ⁡ ( d new , w ) ⁢ P ⁡ ( z ❘ d new , w ) N adj ; and wherein N adj represents an adjusted value of N, N old represents the previous value of N, d new represents the new document d, D new represents a collection of new documents D, W old represents a previous set of terms W, W new represents a new set of terms which replaces W, P old (z) represents the previous value of P(z), and P adj (z) represents a new value of P(z) which replaces the previous value of P(z); and presenting a model parameter from said trained PLSA model, the presented model parameter including at least one of an updated P(z|d) value and an updated P(z|w) value.