NeuralParse a neural model for parsing natural language

Neural models for dealing with symbolic processing are in their infancy. Success thus far can be defined by the parsing of very simple phrases and a small set of words into small, fixed size frames. Many of these systems do not scale well as one increases the number of words or the phrase length. These models are limited with respect to the large number of epochs required to train and the error rates. We address the issue of training and present an analysis which provides a lower bound on the error rate. The approach investigates simple extensions to the basic learning algorithm and makes use of a closest neighbor algorithm for correctness