| dc.contributor | 資管博六 | |
| dc.creator (作者) | 楊亨利;李博逸 | |
| dc.creator (作者) | Yang, Heng-Li;Li, Bo-Yi;Wang, Chen-Shu;Chen, Hong-Yan | |
| dc.date (日期) | 2023-10 | |
| dc.date.accessioned | 2024-07-17 | - |
| dc.date.available | 2024-07-17 | - |
| dc.date.issued (上傳時間) | 2024-07-17 | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/152360 | - |
| dc.description.abstract (摘要) | This study investigates the issue of abusive and hateful language arising from the COVID-19 pandemic, this research employs machine learning techniques to establish a system capable of detecting and rephrasing abusive and hateful language. To begin, a dataset and dictionary specific to abusive and hateful language in Chinese. Subsequently, a two-stage detection model is proposed, with the BERT model yielding the most optimal outcomes. The first stage attains an accuracy of 94.42%, while the second stage achieves an accuracy of 81.48%. | |
| dc.format.extent | 116 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | 2023 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), IEEE | |
| dc.subject (關鍵詞) | Abusive and hate speech; Machine learning; Language model | |
| dc.title (題名) | Can Generative AI Eliminate Speech Harms? A Study on Detection of Abusive and Hate Speech during the COVID-19 Pandemic | |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.1109/ICCE-Asia59966.2023.10326404 | |
| dc.doi.uri (DOI) | https://doi.org/10.1109/ICCE-Asia59966.2023.10326404 | |
