As a format for describing the meaning of natural language sentences, probabilistic logic combines the expressivity of first-order logic with the ability to handle graded information in a principled fashion. But practical probabilistic logic frameworks usually assume a finite domain in which each entity corresponds to a constant in the logic (domain closure assumption). They also assume a closed world where everything has a very low prior probability. These assumptions lead to some problems in the inferences that these systems make. In this paper, we show how to formulate Textual Entail-ment (RTE) inference problems in probabilistic logic in a way that takes the domain closure and closed-world assumptions into account. We evaluate our proposed technique on three RTE datasets, on a synthetic dataset with a focus on complex forms of quantification, on FraCas and on one more natural dataset. We show that our technique leads to improvements on the more natural dataset, and achieves 100% accuracy on the synthetic dataset and on the relevant part of FraCas.
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