Modelling Language Acquisition : Lexical Grounding Through Perceptual Features

A neural network model of language acquisition is introduced, motivated by current research in psychology and linguistics. It uses both extra-linguistic perceptual features and symbolic representations of words. The network learns to auto-associate these inputs to their linguistic labels, as well as to predict the next word in the corpus. This is interpreted to model both the acquisition of a lexicon, and the beginnings of syntax or grammar (word order). Furthermore, the inclusion of the extralinguistic perceptual features is argued to be a form of direct developmental grounding in embodied concepts, which will allow the later learning of more abstract concepts to be grounded indirectly in meaning through relations to the first words. Through this bootstrapping process, future versions of the network may be scalable to large vocabularies, and may bridge the gap between high-dimensional and embodied theories of meaning.