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Towards Semi-Automated Annotation for Prepositional Phrase Attachment

Thadani, Kapil; Rosenthal, Sara; McKeown, Kathleen; Lipovsky, William; Andreas, Jacob

This paper investigates whether high-quality annotations for tasks involving semantic disambiguation can be obtained without a major
investment in time or expense. We examine the use of untrained human volunteers from Amazon’s Mechanical Turk in disambiguating prepositional phrase (PP) attachment over sentences drawn from the Wall Street Journal corpus. Our goal is to compare the performance of these crowdsourced judgments to the annotations supplied by trained linguists for the Penn Treebank project in order to indicate the viability of this approach for annotation projects that involve contextual disambiguation. The results of our experiments show that
invoking majority agreement between multiple human workers can yield PP attachments with fairly high precision, confirming that this crowdsourcing approach to syntactic annotation holds promise for the generation of training corpora in new domains and genres.

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Computer Science
Published Here
April 29, 2013
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