A neural-network architecture for syntax analysis

Artificial neural networks (ANNs), due to their inherent parallelism, offer an attractive paradigm for implementation of symbol processing systems for applications in computer science and artificial intelligence. The paper explores systematic synthesis of modular neural-network architectures for syntax analysis using a prespecified grammar-a prototypical symbol processing task which finds applications in programming language interpretation, syntax analysis of symbolic expressions, and high-performance compilers. The proposed architecture is assembled from ANN components for lexical analysis, stack, parsing and parse tree construction. Each of these modules takes advantage of parallel content-based pattern matching using a neural associative memory. The proposed neural-network architecture for syntax analysis provides a relatively efficient and high performance alternative to current computer systems for applications that involve parsing of LR grammars which constitute a widely used subset of deterministic context-free grammars. Comparison of quantitatively estimated performance of such a system (implemented using current CMOS VLSI technology) with that of conventional computers demonstrates the benefits of massively parallel neural-network architectures for symbol processing applications.