J.R. Doppa, M.S.
Sorower, M. Nasresfahani, J. Irvine, W. Orr, T.G. Dietterich, X. Fern & P.
Tadepalli; JMLR W&CP 20:197–212, 2011.
Learning Rules from Incomplete Examples via Implicit Mention Models
We study the problem of learning general rules from concrete facts extracted from
natural data sources such as the newspaper stories and medical histories. Natural data sources
present two challenges to automated learning, namely, radical incompleteness
and systematic bias
In this paper, we propose an approach that combines simultaneous learning of multiple predictive
rules with diﬀerential scoring of evidence which adapts to a presumed model of data
generation. Learning multiple predicates simultaneously mitigates the problem of radical
incompleteness, while the diﬀerential scoring would help reduce the eﬀects of systematic bias. We
evaluate our approach empirically on both textual and non-textual sources. We further
present a theoretical analysis that elucidates our approach and explains the empirical
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