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2003

Example Selection for Bootstrapping Statistical Parsers

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Example Selection for Bootstrapping Statistical Parsers
This paper investigates bootstrapping for statistical parsers to reduce their reliance on manually annotated training data. We consider both a mostly-unsupervised approach, co-training, in which two parsers are iteratively re-trained on each other’s output; and a semi-supervised approach, corrected co-training, in which a human corrects each parser’s output before adding it to the training data. The selection of labeled training examples is an integral part of both frameworks. We propose several selection methods based on the criteria of minimizing errors in the data and maximizing training utility. We show that incorporating the utility criterion into the selection method results in better parsers for both frameworks.
Mark Steedman, Rebecca Hwa, Stephen Clark, Miles O
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NAACL
Authors Mark Steedman, Rebecca Hwa, Stephen Clark, Miles Osborne, Anoop Sarkar, Julia Hockenmaier, Paul Ruhlen, Steven Baker, Jeremiah Crim
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