Distributional models (Turney and Pantel 2010) use contextual similarity to predict the graded semantic similarity of words and phrases (Landauer and Dumais.
Logic-based representations characterize sentence structure, but do not capture the graded aspect of meaning. Distributional models give graded similarity ...
A hybrid approach that combines logic-based and distributional semantics through probabilistic logic inference in Markov Logic Networks (MLNs) is adopted, ...
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May 26, 2015 · We adopt a hybrid approach that combines logic-based and distributional semantics through probabilistic logic inference in Markov Logic Networks (MLNs).
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“The case for abandoning the categorical view of competence and adopting a probabilistic model is at least as strong in semantics as it is in syntax.
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Logic-based representations characterize sentence structure, but do not capture the graded aspect of meaning. Distributional models give graded similarity ...
This article adopts a hybrid approach that combines logical and distributional semantics using probabilistic logic, specifically Markov Logic Networks and ...
Jul 22, 2016 · ... combination of logical form with distributional as well as resource-based information at the word level, using Markov Logic Networks (MLNs) ...
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The goal of this work is to establish a formal system for combining logic-based meaning representations with weighted information into a single unified ...
This paper describes a mapping between predicates of logical form and points in a vector space. This mapping is then used to project distributional inferences.