A recurrent network was trained from sentence examples to construct symbolic parses of sentence forms. Hundreds of sentences, representing significant syntactic complexity, were formulated and then divided into training and testing sets to evaluate the ability of a recurrent network to learn their structure. The network is shown to generalize well over test sentences and the errors that do remain are found to be of a single type and related to human limitations of sentence processing.
