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Reporting Bias and Knowledge Acquisition

URL to cite or link to: http://hdl.handle.net/1802/28266

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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 challenges it poses for knowledge extraction. We conclude with discussion of approaches to learning commonsense knowledge from text despite this distortion.
Contributor(s):
Jonathan Gordon (1985 - ) - Author
Benjamin D. Van Durme (1979 - ) - Author
Primary Item Type:
Conference proceedings
Language:
English
Subject Keywords:
Learning by reading; knowledge extraction; textual frequencies; information extraction
Sponsor - Description:
National Science Foundation (NSF) - IIS-0916599
Office of Naval Research (ONR) - STTR N00014-10-M-0297
First presented to the public:
10/27/2013
Originally created:
10/27/2013
Date will be made available to public:
2013-10-27   
Original Publication Date:
10/27/2013
Previously Published By:
Association for Computing Machinery
Citation:
Proceedings of the CIKM Automated Knowledge Base Construction Workshop (AKBC 2013).
Extents:
Number of Pages - 5
License Grantor / Date Granted:
Jonathan Gordon / 2014-03-19 19:30:59.815 ( View License )
Date Deposited
2014-03-19 19:30:59.815
Date Last Updated
2014-05-22 10:47:41.098996
Submitter:
Jonathan Gordon

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