  We introduce precision-biased parsing: a parsing task which favors precision
over recall by allowing the parser to abstain from decisions deemed uncertain.
We focus on dependency-parsing and present an ensemble method which is capable
of assigning parents to 84% of the text tokens while being over 96% accurate on
these tokens. We use the precision-biased parsing task to solve the related
high-quality parse-selection task: finding a subset of high-quality (accurate)
trees in a large collection of parsed text. We present a method for choosing
over a third of the input trees while keeping unlabeled dependency parsing
accuracy of 97% on these trees. We also present a method which is not based on
an ensemble but rather on directly predicting the risk associated with
individual parser decisions. In addition to its efficiency, this method
demonstrates that a parsing system can provide reasonable estimates of
confidence in its predictions without relying on ensembles or aggregate corpus
counts.
