In sufficiently limited domains, natural language interaction is possible even in the absence of actual natural language understanding. This is particularly true for goal-directed command and control, where the understanding task can essentially be cast as an Nway classification problem. (Data-driven) semantic inference is an approach to such tasks which in principle allows for unrestricted command/query formulation. It relies on a latent semantic analysis framework, whereby each unconstrained word string is automatically mapped onto the intended action through a semantic classification against the set of supported concepts. The objective of this paper is to compare this approach with other like-minded Nway classification methods, such as based on finite-state grammars or nearest-neighbor techniques. All experiments are conducted in the context of a desktop user interface control task involving 113 different actions. Results illustrate some of the performance and robustness benefits of semantic inference.