政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152639

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152639

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政大典藏 > College of Commerce > Graduate Institute of TIPM > Theses > Item 140.119/152639

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/152639

Title:	以人工智慧分析法律裁決之研究：以域名爭議為例 Applied AI Analysis on Law Case Prediction: Using Domain Name Dispute as Use Case
Authors:	劉珈卉 Liou, Chia-Hui
Contributors:	宋皇志姜國輝 Sung, Huang-Chih Chiang, Kuo-Huie 劉珈卉 Liou, Chia-Hui
Keywords:	域名爭議域名搶註人工智慧法律判決預測小語言模型 BERT Domain Name Dispute Cybersquatting AI Law Case Prediction SLM BERT
Date:	2024
Issue Date:	2024-08-05 13:03:36 (UTC+8)
Abstract:	在現代資訊社會中，隨著網路蓬勃發展，網路詐騙案件不斷增加，對域名爭議的管理單位構成了巨大挑戰。本研究旨在利用人工智慧分析方法，預測域名爭議案件的裁決結果，以開發提高司法資源使用效率的輔助工具。研究問題主要分為兩個層面：一是方法層面，包括小語言模型應用在專門法律領域與避免參數與樣本數量少的過度訓練問題，以及BERT模型的優化方法；二是管理層面，涵蓋使用者、科技發展與道德管理風險，以及未來潛在的商業應用策略分析。在方法層面，本研究證實，透過凍結部分BERT模型層並引入循環神經網路模型如LSTM、GRU、或是注意力機制Transformer層，能有效提升模型的表現能力，其中使用循環神經網絡的效果更好。同時，採用拔靴法、早停策略、學習率調整等統方法，提升模型的泛化能力，避免過度訓練，為預測案件結果提供了可靠基礎。在管理層面，本研究分析了域名爭議人工智慧在早期和成熟市場階段的使用者，並探討了科技、市場和道德倫理等層面對人工智慧應用策略的影響，並且表達本研究者對於域名爭議人工智慧發展的態度，希望能提供未來研究者新的研究方向。本研究的貢獻在於結合人工智慧技術方法和商業管理視角，深入探討域名爭議領域的應用潛力，並提供了實際的模型實作和商業策略分析。然而，研究受樣本與研究資源的限制，未來的研究者應試圖探索更多樣化的標記方法，並深入研究主動學習方法。隨著技術和社會環境的變化，持續關注域名爭議人工智慧的發展將是未來研究的重要方向。總而言之，本研究不僅在技術和方法層面取得了顯著進展，也為推廣域名爭議人工智慧及其在其他專業領域的應用奠定了堅實基礎。 In today's information society, the rapid development of the internet has led to a continuous increase in online fraud cases, posing significant challenges to the management of domain name disputes. This study aims to use artificial intelligence analysis methods to predict the outcomes of domain name dispute cases and develop tools to improve judicial efficiency. The research focuses on two main aspects: methodology and management. Methodologically, the study addresses the application of small language models in specialized legal fields and the optimization of BERT models to avoid overfitting due to limited parameters and sample sizes. By freezing certain BERT layers and incorporating recurrent neural networks like LSTM, GRU, or attention mechanism Transformer layers, performance is enhanced, especially with recurrent networks. Techniques such as bootstrapping, early stopping, and learning rate adjustments further improve generalization, providing a reliable foundation for predicting case outcomes. On the management side, the study analyzes the users of AI in early and mature market stages and explores the impact of technology, market dynamics, and ethics on AI application strategies. It also expresses the researchers' positive stance on AI development in domain name disputes and suggests new research directions. The study combines AI methods with a business management perspective, exploring the application potential in domain name disputes and providing practical models and commercial strategy analysis. Despite limitations in sample size and resources, it suggests future research should explore diverse labeling methods and active learning. Continuous attention to AI development in this field is crucial. This study achieves significant progress in technology and methodology, laying a foundation for broader AI applications in professional fields.
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Description:	碩士國立政治大學科技管理與智慧財產研究所 108364124
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0108364124
Data Type:	thesis
Appears in Collections:	[Graduate Institute of TIPM] Theses

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