My guess is that gmail is using a linear classifier. Both because of the scale of the data, and because up until very recently linear classifiers have been state of the art on text classification.

In the few cases where NN have achieved new state of the art on text, the stanford sentiment analysis work and a few more recent works, a full sentence parse is needed. Sentence parses do achieve 95% accuracy, but only on well structured text in a given domain. Plus, they are hugely time intensive compared to the large scale linear classifiers like vowpal wabbit or sophia-ml.

Regarding perceptrons, a basic perceptron, although it is a linear classifier, will not achieve state of the art. Averaged perceptrons get you closer, but what you really want is a discriminatively trained linear classifier with regularization.

If I had to bet, gmail is probably using something closer to https://code.google.com/p/sofia-ml/ than a NN. Maybe a Googler will surprise me though!