Making sense of words through the eyes of a child

Equipping machines with the ability to understand and use natural language is a difficult task. One aspect underlying this task is the acquisition of language semantics or - to narrow the problem even more - the learning of individual word meanings. A machine, which copes with this problem, has to learn the meaning of a word based on observations of the word in different contexts. Children perform marvellously well in this task. Even though it still remains an open question how children acquire word meanings so efficiently, research in the fields of developmental psychology and neurobiology start to shed light onto some aspects of the underlying learning principles. Designing an artificial system based on such findings may consequently lead a way to overcome the restrictions of existing approaches, thereby striving towards child-like learning abilities. The following thesis addresses this issue. It establishes links between findings from developmental psychology and neurobiology based on which novel computational models for word learning are presented. The models are compared to state of the art and applied in selected word learning scenarios.