The paper discusses the problem of determinizing finite-state
  automata containing large number of $\epsilon$-moves.  Experiments
  with finite-state approximations of natural language grammars often
  give rise to very large automata with a very large number of
  $\epsilon$-moves.  The paper identifies three subset construction
  algorithms which treat $\epsilon$-moves. A number of experiments has
  been performed which indicate that the algorithms differ considerably in
  practice. Furthermore, the experiments suggest that the average number of
  $\epsilon$-moves per state can be used to predict which algorithm is
  likely to perform best for a given input automaton.