Kazakh language based Question Answering system using Deep Learning Approach
| dc.contributor.author | Alua Bilakhanova | |
| dc.date.accessioned | 2024-12-11T10:00:46Z | |
| dc.date.available | 2024-12-11T10:00:46Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | The development and evaluation of question answering (QA) systems in specific languages present unique challenges, and the Kazakh language is no exception. In this study, we address these challenges by developing and evaluating Kazakh language-based QA systems using different deep learning (DL) models. Our objective is to enhance the accuracy and effectiveness of QA in the Kazakh language. We conduct various experiments using translated datasets and implement different models, including BERT, LSTM baseline, and End-to-End Memory Networks (MemN2N). Through rigorous performance evaluation and comparative analysis, we identify the most suitable models for achieving high accuracy in Kazakh language-based QA tasks. It was observed that the MemN2N model achieved the best average accuracy among the three models implemented. This research contributes to the advancement of Kazakh language processing and aims to enhance the accessibility and usability of QA systems in the Kazakh language. | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/1568 | |
| dc.language.iso | en | |
| dc.subject | question answering, Kazakh language, LSTM baseline and End-to-End Memory Networks | |
| dc.title | Kazakh language based Question Answering system using Deep Learning Approach | |
| dc.type | Other |