This work addresses the challenge of automatically unfold transfers of meaning in eventive propositions. For example, if we want to interpret “throw pass” in the context of sports, we need to find the object (“ball”) that transferred some semantic properties to “pass” to make it acceptable as argument for “throw”. We propose a probabilistic model for interpreting an eventive proposition by recovering two additional coupled propositions related to the one under interpretation. We gather the statistics after building a Proposition Store from a document collection, and explore different configurations to couple propositions based on WordNet relations. These coupled propositions compose an actual interpretation of the original proposition with a precision of 0.57, but only for an 18% of samples. If we evaluate whether the interpretation is just useful or not for recovering background knowledge required for interpretation, then results rise up to 0.71 of precision and recall.
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