| dc.contributor | 資訊系 | |
| dc.creator (作者) | 劉昭麟 | |
| dc.creator (作者) | Huang, Ho-Chien;Liu, Chao-Lin | |
| dc.date (日期) | 2025-12 | |
| dc.date.accessioned | 13-Aug-2026 09:17:16 (UTC+8) | - |
| dc.date.available | 13-Aug-2026 09:17:16 (UTC+8) | - |
| dc.date.issued (上傳時間) | 13-Aug-2026 09:17:16 (UTC+8) | - |
| dc.identifier.uri (URI) | https://ah.lib.nccu.edu.tw/item?item_id=184455 | - |
| dc.description.abstract (摘要) | To aid litigants' appeal strategies, this study introduces Disputability (<3), a novel metric quantifying a case's contentiousness by the total number of judicial instances it undergoes. Using a dataset of 52,993 Taiwanese tax judgments, we test the hypothesis that similar cases share similar disputability levels. We compare Judgment-Level and Sentence-Level prediction models, finding that a Judgment-Level approach with multilingual-e5-base embeddings provides the strongest and most practical baseline. While the Sentence-Level model suffered from significant label noise, a high-precision kNN voting strategy proved particularly effective for identifying high-risk, disputable cases. Our work establishes an adaptable and extensible baseline for quantifying case disputability, offering a practical tool for computational law and legal analytics. | |
| dc.format.extent | 115 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Proceedings of the Thirty-Eighth International Conference on Legal Knowledge and Information Systems, IAAIL, pp.306-311 | |
| dc.subject (關鍵詞) | Case Disputability; Legal Judgment Prediction; Baseline Model; Computational Law; Machine Learning | |
| dc.title (題名) | Learning from the judicial journey: A predictive model for case disputability | |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.3233/FAIA251602 | |
| dc.doi.uri (DOI) | https://doi.org/10.3233/FAIA251602 | |
