Concreteness and Corpora: A Theoretical and Practical Study

An increasing body of empirical evidence suggests that concreteness is a fundamental dimension of semantic representation. By implementing both a vector space model and a Latent Dirichlet Allocation (LDA) Model, we explore the extent to which concreteness is reflected in the distributional patterns in corpora. In one experiment, we show that that vector space models can be tailored to better model semantic domains of particular degrees of concreteness. In a second experiment, we show that the quality of the representations of abstract words in LDA models can be im-words in LDA models can be improved by supplementing the training data with information on the physical properties of concrete concepts. We conclude by discussing the implications for computational systems and also for how concrete and abstract concepts are represented in the mind

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