Improving the BERT Model with Proposed Named Entity Recognition Method for Question Answering

Recently, the analysis of textual data has gained importance due to the increase in comments made on web platforms and the need for ready-made answering systems. Therefore, there are many studies in the fields of natural language processing such as text summarization and question answering. In this paper, the accuracy of the B E R T language model is analyzed for the question answering domain, which allows to automatically answer a question asked. Using SQuAD, one of the reading comprehension datasets, the answers to the questions that the B E R T model cannot answer are researched with the proposed Named Entity Recognition method in natural language processing. The accuracy of B E R T models used with the proposed Named Entity Recognition method increases between 1.7% and 2.7%. As a result of the analysis, it is shown that the B E R T model doesn't use Named Entity Recognition technique sufficiently. © 2021 IEEE