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研究生:張正宗
研究生(外文):Cheng-Tsung Chang
論文名稱:電腦輔助簡易刑事判決技術之探討
論文名稱(外文):An Exploration of Computer Assisted Criminal Summary Judgments
指導教授:劉昭麟劉昭麟引用關係
指導教授(外文):Chao-Lin Liu
學位類別:碩士
校院名稱:國立政治大學
系所名稱:資訊科學學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2003
畢業學年度:90
語文別:中文
論文頁數:88
中文關鍵詞:Machine Learning自然語言處理法資訊學
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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
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