Ardac, Hueseyin AvniErdogmus, Pakize2024-08-232024-08-2320241868-64781868-6486https://doi.org/10.1007/s12530-024-09592-7https://hdl.handle.net/20.500.12684/14456Question answering systems are capable of responding to user inquiries using natural language. These systems analyze questions utilizing natural language processing methods and retrieve responses from appropriate data sources using information retrieval techniques. Additionally, text mining and deep network techniques can enhance the effectiveness of question answering systems by providing more accurate and relevant information. In this study, we developed question answering models employing text mining and deep networks. We trained a pre-existing English BERT-base model with the Stanford Question Answering Dataset (SQuADv1.1) utilizing various hyperparameters and fine-tuning values. Our training yielded impressive results with an F1 score of 88.13 and an Exact Match (EM) rate of 80.74, outperforming previous studies in the field. An improvement study was conducted on the Turkish History Question Answering Dataset (THQuADv1.0), which led to the update of the dataset to THQuADv2.0 by adding questions regarding the units of D & uuml;zce University. The pre-trained Turkish BERTurk-base model received training with the THQuADv2.0 dataset utilizing the successful hyperparameters and fine-tuning values obtained in the English model. As a consequence of the training, we developed the BERTDuQuA (BERT D & uuml;zce University Question Answering) model for answering Turkish questions. The BERTDuQuA model demonstrated exceptional performance, achieving an F1 score of 87.10 and an EM of 76.90.en10.1007/s12530-024-09592-7info:eu-repo/semantics/closedAccessBERTBERTurkDeep learningNatural language processingQuestion answeringArticle2-s2.0-85193252084WOS:001226714600001Q2N/A