Prototype-Driven Learning for Sequence Models

  title={Prototype-Driven Learning for Sequence Models},
  author={Aria Haghighi and Dan Klein},
We investigate prototype-driven learning for primarily unsupervised sequence modeling. Prior knowledge is specified declaratively, by providing a few canonical examples of each target annotation label. This sparse prototype information is then propagated across a corpus using distributional similarity features in a log-linear generative model. On part-of-speech induction in English and Chinese, as well as an information extraction task, prototype features provide substantial error rate… CONTINUE READING
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