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Generative and Discriminative Models in Statistical Parsing
This video was recorded at NIPS Workshops, Whistler 2009. Since the earliest work on statistical parsing, a constant theme has been the development of discriminative and generative models with complementary strengths. In this work I'll give a brief history of discriminative and generative models in statistical parsing, focusing on strengths and weaknesses of the various models. I'll start with early work on discriminative history-based models (in particular, the SPATTER parser), moving through early discriminative and generative models based on lexicalized (dependency) representations, through to recent work on conditional-random-field based models. Finally, I'll describe research on semi-supervised approaches that combine discriminative and generative models.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
