 Having surveyed three rather different brands of semantics, we are left with the question of which of these brands serves best in computational linguistic practice. It should be clear from what has been said above that the choice of semantic “tool” will depend on the computational goals of the practitioner. If the goal, for example, is to create a dialogue-based problem-solving system for circuit fault diagnosis, emergency response, medical contingencies, or vacation planning, then an approach based on logical (or at least symbolic) representations of the dialogue, underlying intentions, and relevant constraints and knowledge is at present the only viable option. Here it is of less importance whether the symbolic representations are based on some presumed logical semantics for language, or some theory of mental representation—as long as they are representations that can be reasoned with. The most important limitations that disqualify subsymbolic and statistical representations of meaning for such purposes are their very limited inferential reach and response capabilities. They provide classifications or one-shot inferences rather than reasoning chains, and they do not generate plans, justifications, or extended linguistic responses. However, both neural net techniques and statistical techniques can help to improve semantic processing in dialogue systems, for example by disambiguating word senses,  or recognizing which of several standard plans is being proposed or followed, on the basis of observed utterances or actions.