Our DT-based expansion technique has no notion of dimensions since it works on the word level, and thus does not suffer from this kind of sampling error that is inevitable when representing a large vocabulary with a small fixed number of dimensions or topics.
while vector-space models do a good job at ranking candidates according to their similarity, 2 they fail to efficiently generate a top-ranked list of possible expansions: due to its size, it is infeasible to rank the full vocabulary every time.