Biologically-Motivated Machine Learning of Natural Language and Ontology A Computational Cognitive Model

The individual cognitive science disciplines all have contributions to make to the understanding and modelling of human learning. Our previous research has explored unsupervised learning of phonology, morphology and low-level syntax, as well as basic noun, verb and preposition ontology and semantics, plus musical and speech prosody. Successful applications using a mix of supervised and unsupervised techniques include speech control of equipment, deep web search, confused word spelling correction, multi-lingual semantic models and audio-visual speech recognition. Our current research is focused on doing simultaneous learning of ontology, syntax and semantics by embedding the learner in realistic situations and by developing lowlevel biologically-plausible models of perceptual and cognitive processing.