Data-driven techniques, although commonly used for many natural
  language processing tasks, require large amounts of data to perform
  well. Even with significant amounts of data there is always a long
  tail of infrequent linguistic events, which results in poor
  statistical estimation. To lessen the effect of these unreliable
  estimates, we propose augmenting corpus statistics with linguistic
  knowledge encoded in existing resources.  This paper evaluates the
  usefulness of the information encoded in WordNet for two tasks:
  improving perplexity of a bigram language model trained on very 
  little data, and finding longer-distance correlations in text. 
  Word similarities derived from WordNet are evaluated by comparing
  them to association statistics derived from large amounts of data.
  Although we see the trends we were hoping for, the overall effect is
  small.  We have found that WordNet does not currently have the
  breadth or quantity of relations necessary to make substantial
  improvements over purely data-driven approaches for these two tasks.