Enhanced Linguistic Computational Models and Their similarity with Yager’s Computing with Words

A generalized computational framework for Computing with Words (CWW) using linguistic information (LI) was proposed by Prof. Yager. This framework is based on three steps: translation, manipulation and retranslation. Other works have independently proposed the Linguistic Computational Model (LCM) to express the semantics of LI using Type-1 Fuzzy Sets and Ordinal term sets. The former is called the extension principle, and the latter, the symbolic method. We found that a high degree of similarity can be drawn between these methodologies and Yager’s CWW framework, but no discussion exists in the literature of the similarity drawn between them. Further, the extension principle has a drawback: it considers LI to be equally weighted in the aggregation phase. Also, Intuitionistic fuzzy sets (IFSs) and rough sets have gained popularity to model semantics of LI, but no CWW methodologies have been proposed using them. Thus, the novel contributions of this work are twofold. Firstly, showing the similarity of the linguistic computational models based on extension principle and symbolic method, to the Yager’s generalized CWW framework. Secondly, proposing a new augmented flexible weighting for LCM based on the extension principle and two novel CWW methodologies based on IFS and rough sets.