Even in the world of print, one dictionary is often not enough. Just for English, for example, we can go to standard references like Webster’s Third New International, The American Heritage Dictionary of the English Language, or the Oxford English Dictionary, as well as more specialized lexicons. So how many semantic dictionaries do we really need?
That of course depends on the application. If we are in the situation where our target text data is extremely stable and requires only a general vocabulary, then we might get away with a single semantic dictionary based on a large sample of data processed quite carefully. On the Web, however, we have nothing of the sort, if you haven’t noticed lately.
A sophisticated dictionary that took weeks to build with hairy mathematical algorithms on a reasonable sample of training text may become obsolete overnight. That is not to say that sophisticated dictionaries are unhelpful; but in the merciless competition of the information marketplace, we probably need to be able to pop out a new semantic dictionary based on a gigabyte or more of text in just hours.
Given this kind of turnaround, why would anyone want to rely on a single semantic dictionary with its limited vocabulary and somewhat dated concepts? A new dictionary will of course involve a nontrivial upfront investment, but once a reliable source of tagged data is developed, actual dictionary building can be largely automated. That is the advantage of relying on statistical methods.