Connectionist natural language parsing with BrainC

A close examination of pure neural parsers shows that they either could not guarantee the correctness of their derivations or had to hard-code seriality into the structure of the net. The authors therefore decided to use a hybrid architecture, consisting of a serial parsing algorithm and a trainable net. The system fulfills the following design goals: (1) parsing of sentences without length restriction, (2) soundness and completeness for any context-free language, and (3) learning the applicability of parsing rules with a neural network to increase the efficiency of the whole system. BrainC (backtracktacking and backpropagation in C) combines the well- known shift-reduce parsing technique with backtracking with a backpropagation network to learn and represent typical structures of the trained natural language grammars. The system has been implemented as a subsystem of the Rochester Connectionist Simulator (RCS) on SUN workstations and was tested with several grammars for English and German. The design of the system and then the results are discussed.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.