Abstract
In this paper, we consider the computational modelling of human plausibility judgements for verb-relation-argument triples, a task equivalent to the computation of selectional preferences. Such models have applications both in psycholinguistics and in computational linguistics.
By extending a recent model, we obtain a completely corpus-driven model for this task which achieves significant correlations with human judgements. It rivals or exceeds deeper, resource-driven models while exhibiting higher coverage. Moreover, we show that our model can be combined with deeper models to obtain better predictions than from either model alone.
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