Modeling learning of derivation morphology in a multi-agent simulation

In this paper we simulate language acquisition by focussing on the emergence of derivation morphology. We assume that modeling the learning behavior of humans can help to enhance methods in language processing and retrieval. That is, when we understand how humans learn, we can design systems applying the same techniques in language processing. Children, acquiring the first language, observe the adults' speaking before learning how to express themselves. Learning is a gradual process of acquiring single sounds (phonology), words (lexis), and more complex constructions (morphology, syntax) [1]. A newly learned material is acquired and recognized by already existing knowledge and similarities on each linguistic level contribute to the recognition of new words [2]. Despite these observations common simulation models do not consider morphological and phonological information within automatic learning processes. Here, we extend the scope of these models focusing on derivational morphology as a means of language comprehension and production.