Knowing What to Believe (when you already know something)
Knowing What to Believe (when you already know something)
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发表时间：
2010-08
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通讯作者：
Jeff Pasternack;D. Roth
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作者：
Jeff Pasternack;D. Roth
尽管NLP的许多工作都集中在简单地确定文档的含义上，但我们也必须知道是否应该相信它。事实发现算法试图在语料库中相互竞争的声明中识别“真相”，但未能利用用户的先验知识，并假设真相本身是普遍和客观的，而不是主观的。我们引入了一个框架，将先验知识整合到任何事实发现算法中，将一般的“常识”推理和用户已知的特定事实表达为一阶逻辑，并将其转换为可处理的线性程序。正如我们的结果所显示的，这种方法可以很好地扩展到更大的问题，既减少了错误，又允许系统根据用户而不是大多数人来确定真相。此外，我们引入了三种新的事实发现算法，能够在我们的许多实验中优于现有的事实发现器。
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 fact-finders in many of our experiments.