Understanding natural language using tumbling-frequency phrase chain parsing

Abstract

Of the four primary approaches to processing language by computer, only the parsing approach considers the semantic and syntactic components from the start. In doing so, however, the required resources expand rapidly as the scope of the language processed increases. And as that scope increases, the performance of parsing systems decreases. A natural language processor uses a tumbling-frequency phrase-chain parser as described herein which circumvents this resource-intensive step in parsing, while quickly and almost effortlessly arriving at higher speeds and greater efficiency in natural-language processing with far more accurate results involving a partitioning dictionary and phrase chains, and, more particularly, to the discovery that a small and finite set of 'phrase chains' created using a parsing-based phrase-chain processor accounts for a considerable percentage of human language. The significance of this result is that these phrase chains, once identified, can be programmatically pre-encoded for deep structure (meaning) thus allowing for simpler, faster, and more accurate natural language processing at a semantic level than other known parsing approaches.