An Intelligent Tuned Topic Modelling Questing Answering System as Job Assistant

Artificial Intelligence (AI) based assistant system is executed in all digital websites for performing the Question–Answering (QA) system. However, retrieving the answer to the processed question is more complicated because of the vast data size. So, the current article implemented a novel Reparameterization of Elman Gibbs Modelling (REGM) for the Job assistant framework. The Gibbs modelling features provided better answer retrieval and topic selection outcomes. The trained Job posting data were filtered in the initial hidden phase to remove the noise features. Henceforth, the feature selection proceeded to select the needed features for implementing the QA system. Moreover, the question features were set, and the answer retrieval processing modules and topic selection were implemented. Here, the answer retrieval and topic modelling are processed based on the keyword features. The processed model is tested in the python environment. The highest job assistant accuracy was gained at 94.5%, the highest score compared to other associated studies.