COGPARSE: Brain-Inspired Knowledge-Driven Full Semantics Parsing

Abstract

Humans use semantics during parsing; so should computers. In contrast to phrase structure-based parsers, COGPARSE seeks to determine which meaning-bearing components are present in a text, using world knowledge and lexical semantics for construction grammar form selection, syntactic overlap processing, disambiguation, and confidence calculation. In a brain-inspired way, COGPARSE aligns parsing with the structure of the lexicon, providing a linguistic representation, parsing algorithm, associated linguistic theory, and preliminary metrics for evaluating parse quality. Given sufficient information on nuanced word and construction semantics, COGPARSE can also assemble detailed full-semantics meaning representations of input texts. Beyond the ability to determine which parses are most likely to be intended and to use knowledge in disambiguation, full-semantics parsing enables nuanced meaning representation, learning, summarization, natural language user interfaces, and the taking of action based on natural language input.