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政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 > Item 140.119/157014

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/157014

Title:	Nearest-neighbor search or distance-based search, which is better for finding relevant articles?
Authors:	劉昭麟 Liu, Chao-Lin;Huang, Ho Chien
Contributors:	資訊系
Date:	2025-05
Issue Date:	2025-05-19 11:44:35 (UTC+8)
Abstract:	This study proposes a new approach for identifying relevant articles based on transactions or occurrences. The primary objective of this study is to determine which legal articles can serve as claims or counterarguments given a specific transaction or a particular occurrence. To achieve this, we analyze judicial judgments and assess the relevance between the predicted and actual cited articles. Based on empirical observations from preliminary research, we identify candidate articles using two main approaches: a nearest-neighbor approach and a distance-based method. Furthermore, we investigate we can use hybrid method to optimize the performance and provide a simple implementation of our work on recommendation system, which has the potential for widespread application among both the general public and law firms.
Relation:	Nineteenth International Workshop on Juris-informatics (JURISIN 2025) associated with JSAI International Symposia on AI 2025 (IsAI-2025), JSAI-isAI
Data Type:	conference
DOI 連結:	https://doi.org/10.1007/978-981-96-7071-0_11
DOI:	10.1007/978-981-96-7071-0_11
Appears in Collections:	[資訊科學系] 會議論文

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