text: 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.
  @cite_9: A parsing system returning analyses in the form of sets of grammatical relations can obtain high precision if it hypothesises a particular relation only when it is certain that the relation is correct. We operationalise this technique---in a statistical parser using a manually-developed wide-coverage grammar of English---by only returning relations that form part of all analyses licensed by the grammar. We observe an increase in precision from 75 to over 90 (at the cost of a reduction in recall) on a test corpus of naturally-occurring text.
  @cite_1: We present a novel approach for applying the Inside-Outside Algorithm to a packed parse forest produced by a unification-based parser. The approach allows a node in the forest to be assigned multiple inside and outside probabilities, enabling a set of 'weighted GRs' to be computed directly from the forest. The approach improves on previous work which either loses efficiency by unpacking the parse forest before extracting weighted GRs, or places extra constraints on which nodes can be packed, leading to less compact forests. Our experiments demonstrate substantial increases in parser accuracy and throughput for weighted GR output.
summary: While little research attention was dedicated to the precision-biased task @cite_9 @cite_1 , several studies address the parse-selection task. Yates et.al. perform parse-selection by filtering out parses containing semantically implausible'' relations, where semantic-plausibility is estimated by high co-occurrence of the words in relation in a large corpora ( i.e. , the web).