Bootstrapping syntax from morpho-phonology

It has been a puzzle how the syntax of natural language could be learned from positive evidence alone. Here we present a hybrid neural-network model in which artificial syntactic categories are acquired through unsupervised competitive learning due to grouping together lexical words with consistent phonological endings. These relatively large syntactic categories then become target signals for a feed-forward error-reducing network that learns to pair these lexical items with smaller numbers of function words to form phrases. This hybrid model learns phrasal syntax from positive evidence alone, while covering the essential findings in recent experiments on adult humans learning an artificial language. The model further predicts generalization to novel lexical words (exceptions) from knowledge of function words.