Applying Neutrosophic Natural Language Processing to Analyze Complex Phenomena in Interdisciplinary Contexts

Abstract

The paper addresses the challenge of integrating natural language processing (NLP) with neutrosophic logic to solve problems where uncertainty, ambiguity, and indeterminacy play a critical role. This approach becomes particularly relevant in an increasingly digitalized world, where human-machine interaction requires a deeper understanding of linguistic and contextual nuances. Despite advances in conventional NLP, many existing methodologies lack tools to effectively manage undefined or contradictory elements present in real data. In this context, neutrosophic logic offers an innovative framework to model and analyze complex and multifaceted human perceptions. The study applies a methodology that combines advanced NLP techniques with neutrosophic analytical tools, allowing to processing of texts with simultaneous degrees of truth, falsity, and indeterminacy. The results reveal a significant capacity to identify linguistic patterns in high-uncertainty scenarios, with practical applications in areas such as artificial intelligence, decision-making, and semantic analysis. This approach not only extends the boundaries of traditional NLP, but also provides an adaptable framework for studying complex phenomena in interdisciplinary contexts. Ultimately, this work contributes to the development of more intelligent systems, capable of accurately handling the ambiguity inherent in human language.