A Natural Language Processing Environment for Rule-Based Decision Making with Neutrosophic Logic to Manage Uncertainty and Ambiguity

So far, Salama has shown that the NLP system can be a rule-based system that can apply neutrosophical reasoning to model ambiguity and uncertainty in human language. Salama allows you to reason more than just right/wrong as traditional systems do, adding levels of right, wrong, and uncertainty. Salama follows the structural composition of words based on finite state models such as TWOL and CG2 while using constrained grammars to handle disambiguation and contextbased ambiguity to avoid ambiguity. The system also handles words that are not in the vocabulary. This is a challenging scenario where rule-based approaches often have strengths and weaknesses. We investigate how neutrosophical reasoning can enhance the accuracy and reliability of NLP systems by evaluating their performance on tasks involving subjective information, incomplete information, and complex language features through extensive experiments.