@mastersthesis{Collins3,
  author = "Christopher Collins",
  title = "Head-driven probabilistic parsing for word lattices",
  school = "Department of Computer Science, University of Toronto",
  month = "January",
  year = "2004",
  abstract = "<p>This thesis presents the first application of the state-of-the-art
              head-driven statistical parsing model of Michael Collins as a
              simultaneous language model and parser for large-vocabulary speech
              recognition.  The model is adapted to an online left-to-right
              chart-parser for word lattices, integrating acoustic, <i>n</i>-gram,
              and parser probabilities.</p>
              
              <P>The parser uses structural and lexical dependencies not considered by
              <i>n</i>-gram models, conditioning recognition on more
              linguistically-grounded relationships. By preferring paths through the
              word lattice for which a probable parse exists, word error rate can be
              reduced and important syntactic and semantic relationships can be
              determined in a single step process.</p>
              
              <P>New forms of heuristic search and pruning are employed to improve
              efficiency.  Experiments on the<i> Wall Street Journal</i> treebank
              and lattice corpora show word error rates competitive with the
              standard <i>n</i>-gram language model while extracting additional
              structural information useful for speech understanding.</p>",
  download = "http://www.cs.toronto.edu/~ccollins/thesis2side.pdf"
}