政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/36375

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/36375

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政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/36375

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题名:	電腦輔助簡易刑事判決技術之探討 An Exploration of Computer Assisted Criminal Summary Judgments
作者:	張正宗 Cheng-Tsung Chang
贡献者:	劉昭麟 Chao-Lin Liu 張正宗 Cheng-Tsung Chang
关键词:	自然語言處理法資訊學 Machine Learning
日期:	2001
上传时间:	2009-09-18 18:26:03 (UTC+8)
摘要:	我們以機器學習(Machine Learning)的方法，建立rule-based與case-based的instances，再藉由這些 instances來判斷起訴書的案由和法條，其最好的正確率只比人工建立的rules與cases所判斷的結果低7%而已。由於在我們最基本的方法中，一個判例就會被建立成一個instance，如此，我們將需要大量的空間來儲存instances，針對這個問題，我們也提出了instances clustering與刪除部份較不重要詞這兩個方法，來降低instances所佔的空間，經過簡化的系統的正確率不但與原本未刪減instances時差不多，還可以減少將近一半左右的儲存空間；而且如果我們將這兩個刪減instances的方法混合使用，甚致可以找到一個更好的解，不但能些微提升正確率，還可以把儲存instances所需的空間，降低為原本的四分之一左右。 I apply machine learning techniques to constructing rule-based and case-based reasoning systems. These systems determine the prosecution reasons and applicable articles of lawsuits, and may achieve an accuracy that is just 7% lower than that achieved by a manually-built system. The baseline method constructs one instance for each prior lawsuit, so it takes much space to store all instances. To reduce the storage space, I propose two methods – clustering instance and removing some less important words in instances. The effects of these methods not only maintain the original accuracy, but also reduce the storage space by half. When I integrated all proposed methods, I can even improve the accuracy slightly and reduce the storage space by three quarters. 第一章緒論 1 1.1 簡介 1 1.2 研究動機及方法 1 1.3 本論文的貢獻 4 1.4 本論文的章節架構 4 第二章相關論文回顧和評述 5 2.1 法資訊學 5 2.2 自然語言處理 8 2.3 討論 11 第三章背景知識 14 3.1 中文的法律文件之處理 14 3.2 台灣的簡易刑事判決與資料來源 15 第四章人工建立的判斷方法 21 4.1 Rule-based的方法 21 4.1.1 Rule的建立與語法規則 21 4.1.2 Rule的應用 24 4.2 Case-based的方法 26 第五章 INSTANCE-BASED LEARNING 29 5.1 資料的前處理 29 5.2 Instance的產生 30 5.3 Instances的應用 33 5.3.1 第一種形態的instances之應用 33 5.3.2 第二種形態的instances之應用 35 5.3.3 第三種形態的instances之應用 36 5.4 Instance Clustering 38 5.5 刪掉instances中較不重要的詞 41 5.6 在instances中加入比重 43 第六章實驗結果與分析 46 6.1 正確率的計算 48 6.2 Nearest Neighbor與k-Nearest Neighbor 50 6.3 判斷案由之結果與分析 51 6.4 判斷法條之結果與分析 59 6.5 刪掉instances中較不重要的詞之結果與分析 64 6.6 加入比重的方法及混合三種改善instance的方法之結果與分析 69 第七章結論和展望 84 7.1 結論 84 7.2 未來的展望 86 參考文獻 88
參考文獻:	[1] 何君豪法官。私人討論。 [2] 陳起行, “德國法資訊學,” 法學叢刊, 46(2):79-85, 2001. [3] 林吉鶴, “專家系統應用於命案犯罪現場之研究,” 行政院國科會科資中心, NSC84-2414-H015-001, 1996. [4] 立法院法律全文檢索系統, http://lyfw.ly.gov.tw/gaislaw.htm [5] 自由時報, “法官助理不夠一審法院叫苦,”自由時報 03, 中華民國92年1月9日。 [6] 司法院法學資料全文檢索, http://wjirs.judicial.gov.tw/jirs/ [7] 法務部主管法規資料庫, http://www.moj.gov.tw/f6_frame.htm [8] 法務部全國法規資料庫, http://law.moj.gov.tw/ [9] 法源法律網, http://www.lawbank.com.tw/index.php [10] K. D. Ashley and E. L. Rissland, “But, See, Accord: Generating “Blue Book” Citations in HYPO,” Proceedings of the First International Conference on Artificial Intelligence and Law, pp. 67-74, 1987. [11] G. Brown, “CHINATAX: Exploring Isomorphism with Chinese Law,” Proceedings of the Fourth International Conference on Artificial Intelligence and Law, pp. 175-179, 1993. [12] S. Brüninghaus and K. D. Ashley, “Toward Adding Knowledge to Learning Algorithms for Indexing Legal Cases,” Proceedings of the Seventh International Conference on Artificial Intelligence and Law, pp. 9-17, 1999. [13] S. Brüninghaus and K. D. Ashley, 2001, “Improviing the Representation of Legal Case Texts With Information Extraction Methods,” Proceedings of the Eighth International Conference on Artificial Intelligence and Law, pp. 42-51, 2001. [14] L.-F. Chien, “PAT-tree-based keyword extraction for Chinese information retrieval,” Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50-58, 1997. [15] S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, R. Harshman, “Indexing by Latent Semantic Analysis,” Journal of American Society for Information Science, pp. 391-407, 1990. [16] F. Haft, R. P. Jones and T. Wetter, “A Natural Language Based Legal Expert System for Consultation and Tutoring – The LEX Project,” Proceedings of the First International Conference on Artificial Intelligence and Law, pp. 75-83, 1987. [17] D. S. Hirschberg, “A Linear Space Algorithm for Computing Maximal Common Subsequence,” Communications of the ACM, pp. 341-343, 1975. [18] D. S. Hirschberg, “Algorithms for the Longest Common Subsequence Problem,” Journal of the ACM, pp. 664-675, 1977. [19] N. Hutton, A. Patterson, “Decision Support for Sentencing in a Common Law Jurisdiction,” Proceedings of the Fifth International Conference on Artificial Intelligence and Law, pp. 89-95, 1995. [20] H. Kesong, Y.-C. Wang and F.-F. Wu, “Research on Extracting Subject from Chinese Text,” Proceedings of the Fifth International Workshop on Information Retrieval with Asian Language, pp. 211-212, 2000. [21] C. D. Manning, and H. Schutze, Foundations of Statistical Natural Language Processing, The MIT Press, 1999. [22] E. L. Rissland and K. D. Ashley, “A Case-Based System for Trade Secrets Law,” Proceedings of the First International Conference on Artificial Intelligence and Law, pp. 60-66, 1987. [23] U. J. Schild, “Intelligent Computer Systems for Criminal Sentencing,” Proceedings of the Fifth International Conference on Artificial Intelligence and Law, pp. 229-238, 1995. [24] E. Schweighofer and D. Merkl, “A Learning Technique for Legal Document Analysis,” Proceedings of the Seventh International Conference on Artificial Intelligence and Law, pp. 156-163, 1999. [25] P. Thompson, “Automatic Categorization of Case Law,” Proceedings of the Eighth International Conference on Artificial Intelligence and Law, pp. 70-77, 2001. [26] W.-F. Yang, X. Li, “Chinese Keyword Extraction Based on Max-duplicated String of the Documents,” Proceedings of the 25th annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 439-440, 2002.
描述:	碩士國立政治大學資訊科學學系 90753005 90
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0090753005
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

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