Corpus ID: 8893912

Paraphrase-Driven Learning for Open Question Answering

  title={Paraphrase-Driven Learning for Open Question Answering},
  author={Anthony Fader and Luke Zettlemoyer and Oren Etzioni},
  • Anthony Fader, Luke Zettlemoyer, Oren Etzioni
  • Published in ACL 2013
  • Computer Science
  • We study question answering as a machine learning problem, and induce a function that maps open-domain questions to queries over a database of web extractions. [...] Key Method Our approach automatically generalizes a seed lexicon and includes a scalable, parallelized perceptron parameter estimation scheme. Experiments show that our approach more than quadruples the recall of the seed lexicon, with only an 8% loss in precision.Expand Abstract
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