When is a semantic dictionary good? It really depends on the application, since more specialized content requires more specialized dictionary dimensions. Typically, validation of a given application will involve extensive benchmark testing, often entailing human judgments of the effectiveness of particular statistical characterizations of content.
TextWise does all of this in its product development process, but one would not want to go through an elaboration validation procedure to test the consequences of every small change. As it turns out, there are quick statistical ways to check whether a change is likely to be good or bad. This is no substitute for actual detailed validation at some point, but it allows one to experiment with new ideas at a fairly low cost.
A digital photography metaphor is apt here. One cannot use statistics to identify a prize-winning shot, it is certainly possible to detect major problems without human judgments. For example, areas of maximally white pixels indicate blown highlights, which typically detract from the quality of an image. Similarly, problems with white balance, dynamic range, focus, and other conditions are also readily detectable.
With any huge data object like a semantic dictionary, it is difficult to construct a benchmark that will cover every aspect of it thoroughly. Statistical testing provides an overall sanity check on quality. Otherwise, one would just be buying and selling pigs in a poke.