Analysis of Ambiguity, Vagueness, Fuzziness, Uncertainty, Possibility and Probability in the Natural Language Semantics with Fuzzy Logic

Abstract

Understanding the esotericism of human instinct in their daily life conversation is not enough then a mystery now. This is a bundle of ambiguity, vagueness, fuzziness, uncertainty, possibility and probability as a wrap that humans have built around themselves. With the advancement in artificial Intelligence, natural language processing is more capable now to work with real world and performing intelligent analysises. The real world has interactions between natural and artificial intelligent systems. Despite all it, humans retained their superiority over artificial intelligent systems. The fuzzy Logic can play an important computational role in understanding this intelligence gap in clear dimensions. Logical Semantics, Distributional Semantics and Probabilistic Logic are focused on their intention for better natural language semantic representations. But no single semantic representation fulfills all requirements needed for a satisfactory representation. The objective of the present work has two folds. The first one focused on the understanding of fuzzy logic in two dimensions as an intelligence computational technique and another as mathematical modeling of natural language semantics. The second fold illustrates this intelligence gap with real world examples of natural language processing applications such as Google and Microsoft Translator.