Reporting bias and knowledge acquisition

Authors:
Jonathan Gordon;Benjamin Van Durme
Affiliations:
University of Rochester, Rochester, NY, USA;Johns Hopkins University, Baltimore, MD, USA
Venue:
Proceedings of the 2013 workshop on Automated knowledge base construction
Year:
2013

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Abstract

Much work in knowledge extraction from text tacitly assumes that the frequency with which people write about actions, outcomes, or properties is a reflection of real-world frequencies or the degree to which a property is characteristic of a class of individuals. In this paper, we question this idea, examining the phenomenon of reporting bias and the challenge it poses for knowledge extraction. We conclude with discussion of approaches to learning commonsense knowledge from text despite this distortion.