Grammar Learning by a Self-Organizing Network

This paper presents the design and simulation results of a self(cid:173) organizing neural network which induces a grammar from exam(cid:173) ple sentences. Input sentences are generated from a simple phrase structure grammar including number agreement, verb transitiv(cid:173) ity, and recursive noun phrase construction rules. The network induces a grammar explicitly in the form of symbol categorization rules and phrase structure rules. The purpose of this research is to show that a self-organizing network with a certain structure can acquire syntactic knowledge from only positive (i.e. There has been research on supervised neural network models of language acquisi(cid:173) tion tasks [Elman, 1991, Miikkulainen and Dyer, 1988, John and McClelland, 1988].