  The popularity of applying machine learning methods to computational
linguistics problems has produced a large supply of trainable natural language
processing systems. Most problems of interest have an array of off-the-shelf
products or downloadable code implementing solutions using various techniques.
Where these solutions are developed independently, it is observed that their
errors tend to be independently distributed. This thesis is concerned with
approaches for capitalizing on this situation in a sample problem domain, Penn
Treebank-style parsing.
  The machine learning community provides techniques for combining outputs of
classifiers, but parser output is more structured and interdependent than
classifications. To address this discrepancy, two novel strategies for
combining parsers are used: learning to control a switch between parsers and
constructing a hybrid parse from multiple parsers' outputs.
  Off-the-shelf parsers are not developed with an intention to perform well in
a collaborative ensemble. Two techniques are presented for producing an
ensemble of parsers that collaborate. All of the ensemble members are created
using the same underlying parser induction algorithm, and the method for
producing complementary parsers is only loosely constrained by that chosen
algorithm.
