Parsing English wasn't my intention. I was just generalizing as an example of how neural nets can take incorrect or incomplete information and still make a guess. Their robustness is one of their best features -- so long as the caveats are understood.

For a clearer (i.e., programmatic) example of what I meant, take a look in the game AI example of the neural net module. There, you tell the NN its health, weapons, and the number of enemies it sees and it will suggest an appropriate course of action. While the number of inputs is relatively few, it would be trivial to extend that example to cover a broader range of inputs and still get good answers if your inputs are not complete. Right now, the number of inputs is probably too few to really demonstrate that.

As for genetic algorithms, they would be useless to apply to this sort of problem, but once I work in exposing the error rate in the network, one could use genetic algorithms to design a neural network that could generate relatively accurate answers (much faster than designing by hand) by using a lower error rate as a measure of fitness.