| Abstract | | Although much work in NLP has focused on simply determining what
a document means, we also must know whether or not to believe it.
Fact-finding algorithms attempt to identify the "truth" among
competing claims in a corpus, but fail to take advantage of the
user's prior knowledge and presume that truth itself is universal
and objective rather than subjective. We introduce a framework for
incorporating prior knowledge into any fact-finding algorithm,
expressing both general "common-sense" reasoning and specific facts
already known to the user as first-order logic and translating this
into a tractable linear program. As our results show, this approach
scales well to even large problems, both reducing error and
allowing the system to determine truth respective to the user
rather than the majority. Additionally, we introduce three new
fact-finding algorithms capable of outperforming existing
factfinders in many of our experiments. |