Example Selection for Bootstrapping Statistical Parsers

Mark Steedman, Rebecca Hwa, Stephen Clark, Miles Osborne, Anoop Sarkar, Julia Hockenmaier, Paul Ruhlen, Steven Baker, Jeremiah Crim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper investigates bootstrapping for statistical parsers to reduce their reliance on manually annotated training data. We consider both a mostly-unsupervised approach, cotraining, 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.
Original languageEnglish
Title of host publicationProceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Place of PublicationStroudsburg, PA, USA
PublisherAssociation for Computational Linguistics
Number of pages8
Publication statusPublished - 2003

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