When people read text, they may not understand everything in it. For example, a layman might look at an article from a medical journal and see only that it is about some kind of drug. Someone more familiar with medicine would pick up that this is an experimental drug for treating estrogen-sensitive breast cancer. An expert would note that the drug is an aromatase blocker that performs as well as a standard approved drug in a double-blind controlled trials with a large sample of patients.
If an application seeks simply to distinguish documents about pharmaceuticals from documents about toxic financial assets or about the World Cup tournament in South Africa, then it is enough to understand at a superficial level. If a physician is searching for treatment options for a patient with a recurrence of breast cancer, however, a much deeper grasp of content is called for.
A general type of semantic dictionary covering a broad variety of different subjects is more or less forced to opt for broad coverage by default. Collecting enough training data for two thousand dimensions is a major undertaking; having to do it for twenty thousand dimensions will entail a big commitment of resources that one will have to justify. Still, if such a dictionary is critical for a given application, then we need to make the investment.
In many cases the domain of content to be covered can be quite circumscribed. Accordingly, we probably would be better off to add a fairly small number of dimensions to an existing semantic dictionary rather than build a whole new dictionary from scratch. This will require some special statistical balancing of course, but balancing is what dictionary building is all about.