@mastersthesis{Fazly6,
  author = "Afsaneh Fazly",
  title = "The use of syntax in word completion utilities",
  school = "Department of Computer Science, University of Toronto",
  month = "January",
  year = "2002",
  abstract = "Current word-prediction utilities rely on little more than word
              unigram and bigram frequencies. Can part-of-speech information help?
              To answer this question, we first built a testbench for word 
              prediction; then introduced several new prediction algorithms which
              exploit part-of-speech tag information. We trained the prediction
              algorithms using a very large corpus of English, and in several
              experiments evaluated them according to several performance measures.
              All the algorithms were compared with WordQ, a commercial
              word-prediction program.  Our results confirm that strong word
              unigram and bigram models, collected from a very large corpus, give 
              accurate predictions. All predictors, including that based on word
              unigram statistics, outperform the WordQ prediction algorithm.  The 
              predictor based on word bigrams  works surprisingly well compared to
              the syntactic predictors. Although two of the syntactic predictors 
              work slightly better than the bigram predictor, the ANOVA test shows
              that the difference is not statistically significant.",
  download = "http://ftp.cs.toronto.edu/pub/gh/Fazly-thesis.pdf"
}