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

A. A. Salama; Huda E. Khalid; Ahmed K. Essa; Nadheer M. Ahmed

Authors

A. A. Salama Department of Mathematics and Computer Science, Faculty of Sciences, Port Said University, Port Said, 42526, Egypt.
Huda E. Khalid University of Telafer, The Administration Assistant for the President of the Telafer University, Telafer, Iraq
Ahmed K. Essa University of Telafer, The Administration Assistant for the President of the Telafer University, Telafer, Iraq
Nadheer M. Ahmed Director of the Administrative and Financial Affairs Department President of the Telafer University, Telafer, Iraq;

Keywords:

Neutrosophic Logic, Rule-based NLP, Natural Language Processing, Uncertainty Handling, Ambiguity Resolution, Constraint Grammar, Finite-State Methods, Out-of-Vocabulary Words, Linguistic Rules

Abstract

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.

DOI: 10.5281/zenodo.15052172