Comunicació presentada a: The 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, celebrada del 12 al 14 de juliol de 2012 a Jeju Island, Corea del Sud.

A popular tradition of studying semantic representation has been driven by the assumption that word meaning can be learned from the linguistic environment, despite ample evidence suggesting that language is grounded in perception and action. In this paper we present a comparative study of models that represent word meaning based on linguistic and perceptual data. Linguistic information is approximated by naturally occurring corpora and sensorimotor experience by feature norms (i.e., attributes native speakers consider important in describing the meaning of a word). The models differ in terms of the mechanisms by which they integrate the two modalities. Experimental results show that a closer correspondence to human data can be obtained by uncovering latent information shared among the textual and perceptual modalities rather than arriving at semantic knowledge by concatenating the two.

We acknowledge the support of EPSRC through project grant EP/I032916/1.