Integrating Probabilistic and Fuzzy Logic for Enhanced Natural Language Semantics Interpretation

Abstract

Natural language semantics interpretation is key to AI and computational linguistics growth. Traditional methods struggle with human language's ambiguity and imprecision, making text reading, sentiment analysis, and machine translation difficult. This research innovates by combining probabilistic and fuzzy logic to address natural language's vagueness and uncertainty. We present a probabilistic language semantics architecture that uses fuzzy logic to handle linguistic nuances and gradable categories. We start by building a probabilistic model to assess uncertainty and forecast corpus semantic links. Fuzzy logic is then used to interpret non-binary degrees of truth and conceptual boundaries. In various semantic interpretation tasks, this hybrid model outperforms existing techniques and captures a more sophisticated comprehension of natural language. Our model's adaptability and exceptional performance on datasets from many domains set a new benchmark for natural language semantics interpretation. Our study enables more intuitive and human-like language processing systems, which has broad implications for theoretical linguistics and AI applications.