| dc.contributor | 資訊系 | - |
| dc.creator (作者) | 劉昭麟 | - |
| dc.creator (作者) | Liu, Chao-Lin;Wu, Po-Hsien;Yu, Yi-Ting | - |
| dc.date (日期) | 2025-05 | - |
| dc.date.accessioned | 5-六月-2025 09:05:32 (UTC+8) | - |
| dc.date.available | 5-六月-2025 09:05:32 (UTC+8) | - |
| dc.date.issued (上傳時間) | 5-六月-2025 09:05:32 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/157274 | - |
| dc.description.abstract (摘要) | This report addresses the challenge of limited labeled datasets for developing legal recommender systems, particularly in specialized domains like labor disputes. We propose a new approach leveraging the co-citation of legal articles within cases to establish similarity and enable algorithmic annotation. This method draws a parallel to the concept of case co-citation, utilizing cited articles as indicators of shared legal issues. To evaluate the labeled results, we employ a system that recommends similar cases based on plaintiffs’ accusations, defendants’ rebuttals, and points of disputes. The evaluation demonstrates that the recommender, with finetuned text embedding models and a reasonable BiLSTM module can recommend labor cases whose similarity was measured by the co-citation of the legal articles. This research contributes to the development of automated annotation techniques for legal documents, particularly in areas with limited access to comprehensive legal databases. | - |
| dc.format.extent | 107 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | JSAI International Symposium on Artificial Intelligence, Lecture Notes in Computer Science, Japanese Society for Artificial Intelligence, vol.15692, pp.96–113 | - |
| dc.title (題名) | Labeling Case Similarity based on Co-Citation of Legal Articles in Judgment Documents with Empirical Dispute-Based Evaluation | - |
| dc.type (資料類型) | conference | - |
| dc.identifier.doi (DOI) | 10.1007/978-981-96-7071-0_7 | - |
| dc.doi.uri (DOI) | https://doi.org/10.1007/978-981-96-7071-0_7 | - |
