Guven Z.A.Unalir M.O.2023-01-122023-01-1220219781665429085https://doi.org/10.1109/UBMK52708.2021.9558992https://hdl.handle.net/11454/796956th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- -- 176826Recently, 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 IEEEtr10.1109/UBMK52708.2021.9558992info:eu-repo/semantics/closedAccessBERTData AnalysisNamed Entity RecognitionNatural Language ProcessingQuestion AnsweringAnalysis of textual datumBERTData analyseLanguage modelNamed entity recognitionQuestion AnsweringReading comprehensionRecognition methodsT-modelText SummarisationNatural language processing systemsConference Object2042082-s2.0-85125832170N/A