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題名 階層式分群法在民事裁判要旨分群上之應用
An Application of Hierarchical Clustering of Documents for Civil Judgments
作者 何君豪
Ho,Jim How
貢獻者 劉昭麟<br>高照明
Liu,Chao Lin<br>Gao,Zhao Ming
何君豪
Ho,Jim How
關鍵詞 人工智慧與法律
階層式分群法
聚合法
分群
AI&Law
Hierarchical Method
Agglomerative Approach
Cluster
日期 2006
上傳時間 17-Sep-2009 13:51:58 (UTC+8)
摘要 司法院經常聘請資深的法官將民事裁判中具有參考價值的法律意見摘錄出來，製作成民事裁判要旨，民事裁判要旨可作為法官審理類似案件時的辦案參考，因此，在司法實務上民事裁判的搜尋為不可或缺的工作。然隨著資訊科技的發達及裁判數量的累積，民裁判要旨的搜尋結果可能多達數百篇，造成法官須耗費大量的時間在民事裁判要旨的閱讀上，如果能利用資料探勘的技術將搜尋到的民事裁判要旨加以分群，且分群的正確性又可達到一定旳水準，便可節省法官閱讀民事裁判要旨的時間。在本研究中我們嘗試將資料探勘技術中的階層式分群法應用在民事裁判要旨的分群上，並將法律條文所出現的用語作為加權的主關鍵字評估可否改善分群的效果，以探討資料探勘技術中的階層式分群法應用在民事裁判要旨分群上的可行性與成效。
Judicial Yuan often invites senior civil judges to extract legal opinions from civil judgments for making the purports of civil judgments. The purports of civil judgments can be consulted as trial judges handle the similar cases, therefore, in judicial practices, it is an indispensable work for civil judges to search the purports of civil judgments. However, with the development of information technology and the cumulative number of judgments, the number of search results may be as high as hundreds, civil judges must have spent a lot of time reviewing of the purports of civil judgments. If we can utilize data mining technologies to cluster the search results, and the accuracy of clustering can be attained to a certain standard, it will save civil judges a lot of time on reviewing the purports of civil judgments. In this study we attempt to apply hierarchical method on the clustering of the purports of civil judgments, and adjust the weights of main keywords derived from frequently used vocabulary of legal provisions to assess the feasibility and effectiveness of application of hierarchical method on clustering of the purports of civil judgments.
參考文獻 [1] 司法院法學檢索系統法官版，http://njirs.judicial.gov.tw/Index.htm
[2] 國際人工智慧與法律協會網站,http://www.iaail.org/index.html
[3] HowNet電子詞典1999年版本，http://www.keenage.com/
[4] 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。
[5] 洪鵬翔，中文新聞自動群聚，國立清華大學資訊工程學系碩士論文，2000。
[6] 張正宗，電腦輔助簡易刑事判決技術之探討，國立政治大學資訊科學系，碩士論文，台北，台灣，2003。
[7] 郭盛楊，以顧客交易特徵為基礎的顧客分群方法，朝陽科技大學資訊管理系碩士論文，pp.64-66，台中，台灣，2004。
[8] 曾憲雄、蔡秀滿、蘇東興、曾秋蓉、王慶堯，資料探勘，旗標，2005。
[9] 廖鼎銘，觸犯多款法條之賭博與竊盜案件的法院文書的分類與分析，國立政治大學資訊科學系，碩士論文，台北，台灣，2004。
[10] 謝淳達，利用詞組檢索中文訴訟文書之研究，國立政治大學資訊科學系，碩士論文，台北，台灣，2005。
[11] 羅淑娟、柯秀奎，應用Raz & Yaung方法論於文件自動分群，第一屆台灣作業研究會暨2004年科技與管理學研討會，pp.1199-1206，2004。
[12] K. D. Ashley and E. L. Rissland, But, see, accord: Generating Blue Book citations in HYPO, Proc. of the 1st Int`l Conf. on Artificial Intelligence and Law, pp.67-74, 1987.
[13] S. Brüninghaus and K. D. Ashley, Finding factors: Learning to classify case opinions under abstract fact categories, Proceedings of the Sixth International Conference on Artificial Intelligence and Law, pp.123-131, 1997.
[14] S. Brüninghaus and K. D. Ashley, Toward adding knowledge to learning algorithms for indexing legal cases", Proc. of the 7th Int`l Conf. on Artificial Intelligence and Law, pp. 9-17, 1999.
[15] L. Kaufman & P.J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, 1990.
[16] C. L. Liu, C. T. Chang, and J. H. Ho. Classification and clustering for case-based criminal summary judgment, Proceedings of the Ninth International Conference on Artificial Intelligence and Law, pp.252-261, 2003.
[17] C.-L. Liu, C.-T. Chang, and J.-H. Ho. Case instance generation and refinement for case-based criminal summary judgments in Chinese, Journal of Information Science and Engineering 20(4), pp.783-800, 2004.
[18] C. D. Manning & H. Schutze, Foundations of Statistical Natural Language Processing, The MIT Press, 1999.
[19] D.V. McDERMOTT, Temporal Logic for Reasoning about Processes and Plans, Cognitive Science 6, pp101-155, 1982.
[20] H.-T. Pu & L.-F. Chien, Integrating log-based and text-based Methods Towards Automatic Web Htesaurus Construction, supported by the National Science Council (NSC), ROC, under the contract NSC 89-2413-H-128-006.
[21] S. Russel & P. Norvig, Artificial Intelligence - A Modern Approach, Prentice Hall, 1995.
[22] E. Schweighofer & D. Merkl, A learning technique for legal document analysis, Proceedings of the Seventh International Conference on Artificial Intelligence and Law, pp.156-163, 1999.
[23] R. Schalkoff, Similarity measures, matching techniques, and scal-space 14 approaches, Pattern Recognition: Statistical, Structural and Neural Approaches, pp.329-330, John Wiley & sons, Inc., New York, 1992.
[24] O. Zamir and O. Etzioni, Web document clustering: a feasibility demonstration, Proceedings of the 19th International ACM SIGIR Conference on Research and Development in Information Retrieval , pp.46-54, 1998.
描述 碩士
國立政治大學
資訊科學學系
89753005
95
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0089753005
資料類型 thesis
dc.contributor.advisor 劉昭麟<br>高照明zh_TW
dc.contributor.advisor Liu,Chao Lin<br>Gao,Zhao Mingen_US
dc.contributor.author (Authors) 何君豪zh_TW
dc.contributor.author (Authors) Ho,Jim Howen_US
dc.creator (作者) 何君豪zh_TW
dc.creator (作者) Ho,Jim Howen_US
dc.date (日期) 2006en_US
dc.date.accessioned 17-Sep-2009 13:51:58 (UTC+8)-
dc.date.available 17-Sep-2009 13:51:58 (UTC+8)-
dc.date.issued (上傳時間) 17-Sep-2009 13:51:58 (UTC+8)-
dc.identifier (Other Identifiers) G0089753005en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/32616-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 89753005zh_TW
dc.description (描述) 95zh_TW
dc.description.abstract (摘要) 司法院經常聘請資深的法官將民事裁判中具有參考價值的法律意見摘錄出來，製作成民事裁判要旨，民事裁判要旨可作為法官審理類似案件時的辦案參考，因此，在司法實務上民事裁判的搜尋為不可或缺的工作。然隨著資訊科技的發達及裁判數量的累積，民裁判要旨的搜尋結果可能多達數百篇，造成法官須耗費大量的時間在民事裁判要旨的閱讀上，如果能利用資料探勘的技術將搜尋到的民事裁判要旨加以分群，且分群的正確性又可達到一定旳水準，便可節省法官閱讀民事裁判要旨的時間。在本研究中我們嘗試將資料探勘技術中的階層式分群法應用在民事裁判要旨的分群上，並將法律條文所出現的用語作為加權的主關鍵字評估可否改善分群的效果，以探討資料探勘技術中的階層式分群法應用在民事裁判要旨分群上的可行性與成效。zh_TW
dc.description.abstract (摘要) Judicial Yuan often invites senior civil judges to extract legal opinions from civil judgments for making the purports of civil judgments. The purports of civil judgments can be consulted as trial judges handle the similar cases, therefore, in judicial practices, it is an indispensable work for civil judges to search the purports of civil judgments. However, with the development of information technology and the cumulative number of judgments, the number of search results may be as high as hundreds, civil judges must have spent a lot of time reviewing of the purports of civil judgments. If we can utilize data mining technologies to cluster the search results, and the accuracy of clustering can be attained to a certain standard, it will save civil judges a lot of time on reviewing the purports of civil judgments. In this study we attempt to apply hierarchical method on the clustering of the purports of civil judgments, and adjust the weights of main keywords derived from frequently used vocabulary of legal provisions to assess the feasibility and effectiveness of application of hierarchical method on clustering of the purports of civil judgments.en_US
dc.description.tableofcontents 謝　　辭 i
摘　　要 iii
Abstract iv
目　　錄 v
表 目 錄 viii
圖 目 錄 x
第一章　緒論 1
1.1　研究動機 1
1.2　研究目的 2
1.3　論文架構 3
第二章　文獻探討 4
2.1　人工智慧與法律 4
2.2　中文斷詞處理 6
2.3　文章近似度的計算公式 8
2.4　群集法的分類簡介 9
2.5　法律關鍵詞的擷取與加權 10
第三章　研究方法 13
3.1　民事裁判要旨資料的來源 13
3.2　民事裁判要旨的表示方法 14
3.3　計算民事裁決要旨近似度的演算法 14
3.3.1採用取min值法計算近似度 15
3.3.2採用matching coefficient法計算近似度 16
3.3.3採用Jaccard coefficient法計算近似度 16
3.3.4採用cosine公式計算近似度 17
3.4　階層式分群法(Hierarchical Clustering) 18
3.4.1階層式分群法的演算法 18
3.4.2文章合併後與其他群集近似度的計算 22
3.4.3階層式分群演算法範例 23
3.5　對於法條常用詞彙調整權重的方法 26
第四章　實驗評估 28
4.1　實驗說明與評估準則 28
4.1.1實驗流程說明 28
4.1.2分群成效的評估 31
4.2　各種文章合併演算法對於分群結果的影響 36
4.2.1各種文章合併演算法對於Cosine分群法的影響 37
4.2.1.1加權平均法 37
4.2.1.2最大值法 38
4.2.1.3最小值法 39
4.2.1.4比較分析 40
4.2.2各種文章合併演算法對於Jaccard分群法的影響 41
4.2.2.1加權平均法 41
4.2.2.2最大值法 42
4.2.2.3最小值法 43
4.2.2.4比較分析 44
4.2.3各種文章合併演算法對於matching分群法的影響 45
4.2.3.1加權平均法 45
4.2.3.2最大值法 46
4.2.3.3最小值法 47
4.2.3.4比較分析 48
4.2.4各種文章合併演算法對於取min值分群法的影響 49
4.2.4.1加權平均法 49
4.2.4.2最大值法 50
4.2.4.3最小值法 51
4.2.4.4比較分析 52
4.2.5各種文章合併法對分群演算法影響的綜合比較分析 53
4.3　各種分群演算法應用於民事裁判要旨分群成效的評估 54
4.4　對於法律關鍵字加權對分群成效的影響 56
4.4.1以112篇民事裁判要旨實驗的結果 57
4.4.2以109篇民事裁判要旨實驗的結果 58
4.4.3比較分析 59
第五章　結論與未來研究工作 61
5.1　結論 61
5.2　未來研究工作 62
參考文獻 64
附錄一：論文口試會後補述 66
附錄二：論文發表 69		zh_TW
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dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0089753005en_US
dc.subject (關鍵詞) 人工智慧與法律zh_TW
dc.subject (關鍵詞) 階層式分群法zh_TW
dc.subject (關鍵詞) 聚合法zh_TW
dc.subject (關鍵詞) 分群zh_TW
dc.subject (關鍵詞) AI&Lawen_US
dc.subject (關鍵詞) Hierarchical Methoden_US
dc.subject (關鍵詞) Agglomerative Approachen_US
dc.subject (關鍵詞) Clusteren_US
dc.title (題名) 階層式分群法在民事裁判要旨分群上之應用zh_TW
dc.title (題名) An Application of Hierarchical Clustering of Documents for Civil Judgmentsen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1] 司法院法學檢索系統法官版，http://njirs.judicial.gov.tw/Index.htmzh_TW
dc.relation.reference (參考文獻) [2] 國際人工智慧與法律協會網站,http://www.iaail.org/index.htmlzh_TW
dc.relation.reference (參考文獻) [3] HowNet電子詞典1999年版本，http://www.keenage.com/zh_TW
dc.relation.reference (參考文獻) [4] 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。zh_TW
dc.relation.reference (參考文獻) [5] 洪鵬翔，中文新聞自動群聚，國立清華大學資訊工程學系碩士論文，2000。zh_TW
dc.relation.reference (參考文獻) [6] 張正宗，電腦輔助簡易刑事判決技術之探討，國立政治大學資訊科學系，碩士論文，台北，台灣，2003。zh_TW
dc.relation.reference (參考文獻) [7] 郭盛楊，以顧客交易特徵為基礎的顧客分群方法，朝陽科技大學資訊管理系碩士論文，pp.64-66，台中，台灣，2004。zh_TW
dc.relation.reference (參考文獻) [8] 曾憲雄、蔡秀滿、蘇東興、曾秋蓉、王慶堯，資料探勘，旗標，2005。zh_TW
dc.relation.reference (參考文獻) [9] 廖鼎銘，觸犯多款法條之賭博與竊盜案件的法院文書的分類與分析，國立政治大學資訊科學系，碩士論文，台北，台灣，2004。zh_TW
dc.relation.reference (參考文獻) [10] 謝淳達，利用詞組檢索中文訴訟文書之研究，國立政治大學資訊科學系，碩士論文，台北，台灣，2005。zh_TW
dc.relation.reference (參考文獻) [11] 羅淑娟、柯秀奎，應用Raz & Yaung方法論於文件自動分群，第一屆台灣作業研究會暨2004年科技與管理學研討會，pp.1199-1206，2004。zh_TW
dc.relation.reference (參考文獻) [12] K. D. Ashley and E. L. Rissland, But, see, accord: Generating Blue Book citations in HYPO, Proc. of the 1st Int`l Conf. on Artificial Intelligence and Law, pp.67-74, 1987.zh_TW
dc.relation.reference (參考文獻) [13] S. Brüninghaus and K. D. Ashley, Finding factors: Learning to classify case opinions under abstract fact categories, Proceedings of the Sixth International Conference on Artificial Intelligence and Law, pp.123-131, 1997.zh_TW
dc.relation.reference (參考文獻) [14] S. Brüninghaus and K. D. Ashley, Toward adding knowledge to learning algorithms for indexing legal cases", Proc. of the 7th Int`l Conf. on Artificial Intelligence and Law, pp. 9-17, 1999.zh_TW
dc.relation.reference (參考文獻) [15] L. Kaufman & P.J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, 1990.zh_TW
dc.relation.reference (參考文獻) [16] C. L. Liu, C. T. Chang, and J. H. Ho. Classification and clustering for case-based criminal summary judgment, Proceedings of the Ninth International Conference on Artificial Intelligence and Law, pp.252-261, 2003.zh_TW
dc.relation.reference (參考文獻) [17] C.-L. Liu, C.-T. Chang, and J.-H. Ho. Case instance generation and refinement for case-based criminal summary judgments in Chinese, Journal of Information Science and Engineering 20(4), pp.783-800, 2004.zh_TW
dc.relation.reference (參考文獻) [18] C. D. Manning & H. Schutze, Foundations of Statistical Natural Language Processing, The MIT Press, 1999.zh_TW
dc.relation.reference (參考文獻) [19] D.V. McDERMOTT, Temporal Logic for Reasoning about Processes and Plans, Cognitive Science 6, pp101-155, 1982.zh_TW
dc.relation.reference (參考文獻) [20] H.-T. Pu & L.-F. Chien, Integrating log-based and text-based Methods Towards Automatic Web Htesaurus Construction, supported by the National Science Council (NSC), ROC, under the contract NSC 89-2413-H-128-006.zh_TW
dc.relation.reference (參考文獻) [21] S. Russel & P. Norvig, Artificial Intelligence - A Modern Approach, Prentice Hall, 1995.zh_TW
dc.relation.reference (參考文獻) [22] E. Schweighofer & D. Merkl, A learning technique for legal document analysis, Proceedings of the Seventh International Conference on Artificial Intelligence and Law, pp.156-163, 1999.zh_TW
dc.relation.reference (參考文獻) [23] R. Schalkoff, Similarity measures, matching techniques, and scal-space 14 approaches, Pattern Recognition: Statistical, Structural and Neural Approaches, pp.329-330, John Wiley & sons, Inc., New York, 1992.zh_TW
dc.relation.reference (參考文獻) [24] O. Zamir and O. Etzioni, Web document clustering: a feasibility demonstration, Proceedings of the 19th International ACM SIGIR Conference on Research and Development in Information Retrieval , pp.46-54, 1998.zh_TW