Grey relational analysis and natural language Processing

Abstract

This paper investigates validity of using grey relational analysis (GRA) for natural language processing (NLP). The domain of NLP is one associated with inherent vagueness and abstraction, with many sub-domains all invoking their own associated uncertainties. Regardless of the particularisation, the main objective is understanding and making sense of linguistic lexicon. The inferencing and understanding of sentiment from natural language has been investigated thoroughly, however, the use of grey system theory in conjunction with NLP has yet to be explored in any great detail. Ergo, an introductory investigation into the effectiveness of using GRA on and with regards to NLP. This paper describes the feasibility of using grey system methodologies and tools, specifically the use of grey incidence, to provide a means for analysis of a sequence's geometric curve. The use of GRA provides one with the ability to inspect and infer sequences of data. Using this notion and by having a sequence represented as an input stream, it can be correlated against possible output commands. The use of grey incidence for quantifying and evaluating the correlation between what is inputted, against what output it is most similar to, is novel and should provide an additional facet to grey system theory.