Precision-biased Parsing and High-Quality Parse Selection Academic Article uri icon


  • 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 …

publication date

  • January 1, 2012