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題名 透過起訴書輔助法院判決－以竊盜罪為例
Using Indictments to Assist Judges in Judging – A Case Study with Offenses of Larceny作者 李右元
Lee, Yu-Yuan貢獻者 劉昭麟
Liu, Chao-Lin
李右元
Lee, Yu-Yuan關鍵詞 判決結果預測
類似案件推薦
法律科技應用
輔助判決
深度學習
judgement predicting
similar cases recommending
the application of LegalTech
judgement assistance
deep learning日期 2021 上傳時間 2-Mar-2021 14:32:02 (UTC+8) 摘要 近年來，隨著技術的成長，自然語言處理的工作在不同的領域間發展，其中亦包含法律面向。在台灣，法律與科技的應用目前仍在起步的階段，有些社群活動亦開始著重於此面向，例如法律科技黑客松。 就台灣的刑事訴訟而言，案件會先經由檢察官的偵查，若被告遭受起訴處分，案件才會移交由法官進行審理及判決。而訴訟的過程往往曠日廢時，其潛在原因可能是被告對於判決結果的不符而上訴。此外，因應國民法官的推動，台灣可能逐步走向參審制的判決。相較於現任法官，國民法官可能沒有法律相關的知識或判決相關的經驗，使其對於最終判決的影響可能較不客觀。 因此本實驗以輔助判決為目標，其對象可以是一般民眾、被告、國民法官，甚至是現任法官及律師等。實驗結合判決結果預測以及類似案件推薦兩部分工作，除了提供使用者可能的判決結果，亦透過「與預測結果相符」及「與預測結果不符」二類相似案件，提供不同面向的案件做為比較及參考。 在過去判決結果預測的相關實驗中，多是以裁判書作為實驗語料。我們則將訴訟流程往回推一步，採用起訴書作為主要語料，希望能在判決結果確定前就對於案件提供相關輔助功能。而在起訴書數量較少，且判決類別不平均的情況下，判決結果預測的實驗目前最高平均值能達到0.665 的Macro_F1分數，在類似案件推薦的實驗中也確實能透過起訴書內容，找出類似案件。
With the advance of science and technology, the works of natural language processing have been growing in many different fields in recent years. In Taiwan, the applications between law and technology are still in their infancy, while some communities have begun to focus on these aspect, such as Legaltech Hackathon.In terms of criminal proceeding in Taiwan, the case will first be investigated by the prosecutor. If the defendant is charged, the case will be transferred to the judge for trial and judgement. But the judicial proceeding is usually time-consuming, and the reason may be that the prosecutor or defendant appealed against the judgement. Furthermore, with the promotions of citizen judge system, Taiwan may gradually move towards a lay judge system. Compared with professional judges, citizen judges may not have legal knowledge or judgement-related experience, which may lead to the less objective judgements.Therefore, the goal of this experiment is to assist judgements, and its objects can be defendants, citizen judges, and even professional judges and lawyers. Our experiment combines two parts of the work, which are judgement predicting and similar cases recommending. In addition to providing users with possible judgements, the two types of similar cases "consistent with the prediction" and "not consistent with the prediction" are also provided for comparison of different aspects of the case.In the past related experiments, court’s judgements were mostly used as experiment corpus. To provide relevant auxiliary functions for the case before the judgement confirmed, we use the indictments as our main corpus. However, with small amount of indictments and unbalanced judgement types, our judgement predicting can still have 0.665 of macro f-1 score, and the similar cases can indeed be found through the content of the indictment.參考文獻 [1] C. Cortes and V. Vapnik, “Support-vector networks”, Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.[2] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”, North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL ’19, vol. 1, pp. 4171-4186, Jun. 2019.[3] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “LIBLINEAR: A Library for Large Linear Classification”, Journal of Machine Learning Research, JMLR, vol. 9, pp. 1871-1874, Aug. 2008. Available: http://www.csie.ntu.edu.tw/~cjlin/liblinear/[4] Z. S. Harris, “Distributional Structure”, WORD, vol. 10, no. 2-3, pp. 146-162, 1954.[5] A. Heidarian, M. J. Dinneen, “A Hybrid Geometric Approach for Measuring Similarity Level Among Documents and Document Clustering”, IEEE Second International Conference on Big Data Computing Service and Applications, Mar. 2016.[6] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory”, Neural Computation, vol. 9, no. 8, pp. 1735-1780, Nov. 1997.[7] J. D. Lafferty, A. McCallum, and F. C. N. Pereira, “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data”, International Conference on Machine Learning, ICML ’01, pp. 282-289, Jun. 2001.[8] N. Landwehr, M. Hall, and E. Frank, “Logistic Model Trees”, Machine Learning, vol. 59, no. 1-2, pp. 161-205, May. 2005.[9] Q. Le, T. Mikolov, “Distributed Representations of Sentences and Documents”, International Conference on Machine Learning, ICML ’14, vol.32, pp. 1188-1196, June 2014.Available: https://radimrehurek.com/gensim/models/doc2vec.html[10] P.-H. Li, T.-J. Fu, and W.-Y. Ma, “Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER”, AAAI Conference on Artificial Intelligence, AAAI ’20, vol.34, no. 5, Apr. 2020. Available: https://github.com/ckiplab/ckiptagger/[11] S. Long, C. Tu, Z. Liu, and M. Sun, “Automatic Judgment Prediction via Legal Reading Comprehension”, Chinese Computational Linguistics, CCL ’19, Lecture Notes in Computer Science, vol. 11856, pp. 558-572, Oct. 2019.[12] W.-Y. Ma, and K.-J. Chen, “Introduction to CKIP Chinese Word Segmentation System forthe First International Chinese Word Segmentation Bakeoff”, the Second SIGHAN Workshop on Chinese Language Processing, pp. 168-171, Jul. 2003.Available: http://ckipsvr.iis.sinica.edu.tw/[13] C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, P. Inc, S. J. Bethard, D. Mcclosky, “The Stanford CoreNLP Natural Language Processing Toolkit”, Association for Computational Linguistics: System Demonstrations, pp. 55-60. 2014.Available: https://stanfordnlp.github.io/CoreNLP/[14] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space”, International Conference on Learning Representations, ICLR ’13, Jan. 2013. Available: https://radimrehurek.com/gensim/models/word2vec.html[15] T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality”, Neural Information Processing Systems, NIPS ’13, vol. 2, pp. 3111-3119, 2013[16] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, É. Duchesnay, “Scikit-learn: Machine Learning in Python”, Journal of Machine Learning Research, JMLR, vol. 12, pp. 2825-2830, 2011.Available: https://scikit-learn.org/stable/[17] O.-M. Şulea, M. Zampieri, M. Vela, and J. van Genabith, “Predicting the Law Area and Decisions of French Supreme Court Cases”, Recent Advances in Natural Language Processing, RANLP ’17, pp.716-722, Sep. 2017.[18] T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. -v. Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. -L. Scao, S. Gugger, M. Drame, Q. Lhoest, and A. -M. Rush, “HuggingFace`s Transformers: State-of-the-art Natural Language Processing”, arXiv-1910.03771, 2019.Available: https://github.com/huggingface/transformers/[19] H. Zhong, Z. Guo, C. Tu, C. Xiao, Z. Liu, and M. Sun, “Legal Judgment Prediction via Topological Learning”, Empirical Methods in Natural Language Processing, EMNLP ’18, pp. 3540-3549, Oct.-Nov. 2018.[20] fxsjy, “jieba”. Available: https://github.com/fxsjy/jieba/[21] ldkrsi, “jieba-zh_TW”. Available: https://github.com/ldkrsi/jieba-zh_TW/[22] Chollet, Francois and others., “Keras”. Available: https://keras.io/[23] 井上正仁，〈裁判員制度與刑事司法 — 兩人三腳 十年之旅(上)〉，司法周刊，第1996期，頁2，2020年3月。[24] 井上正仁，〈裁判員制度與刑事司法 — 兩人三腳 十年之旅(中)〉，司法周刊，第1997期，頁2-3，2020年4月。[25] 井上正仁，〈裁判員制度與刑事司法 — 兩人三腳 十年之旅(上)〉，司法周刊，第1998期，頁3，2020年4月。[26] 王業沛、宋夢姣、王譞、趙志宏，〈基於深度學習的判決結果傾向性分析〉，計算機應用研究，第36卷，第2期，頁335-338，2019年2月。[27] 沈宜生，〈職業法官與平民法官在法庭的互動以及判決意見的差異(上)〉，司法周刊，第1882期，頁2-3，2017年12月。[28] 沈宜生，〈職業法官與平民法官在法庭的互動以及判決意見的差異(下)〉，司法周刊，第1883期，頁2-3，2018年1月。[29] 林琬真、郭宗廷、張桐嘉、顏厥安、陳昭如、林守德，〈利用機器學習於中文法律文件之標記、案件分類及量刑預測〉，中文計算語言學期刊，第17:4期，頁49-67，2012年12月。[30] Lawsnote，〈法律科技黑客松2019〉，網址：https://hackathon.lawsnote.com/[31] 司法院，〈裁判書開放資料下載頁面〉，網址：http://data.judicial.gov.tw/[32] 司法院，〈量刑趨勢建議系統〉，網址：https://sen.judicial.gov.tw/pub_platform/sugg/index.html[33] 法務部，〈法務部檢察機關公開書類查詢系統〉，網址：https://psue.moj.gov.tw/psiqs/index.jsp/[34] colah, “Understanding LSTM Networks”, Aug. 2015.Retrieved from: https://colah.github.io/posts/2015-08-Understanding-LSTMs/[35] copperking, “Please explain Support Vector Machines (SVM) like I am a 5 year old”, Jan. 2013.Retrieved from: https://www.reddit.com/r/MachineLearning/comments/15zrpp/please_explain_support_vector_machines_svm_like_i/?utm_source=share&utm_medium=web2x&context=3[36] KiroSummer, “How to implement a different version of BiLSTM”, Mar. 2018.Retrieved from: https://discuss.pytorch.org/t/how-to-implement-a-different-version-of-bilstm/14698[37] 徐蘭萍，〈臺灣新北地方法院108年簡字第61號刑事判決〉，司法院法學資料檢索系統，2019年2月。參考來源：https://law.judicial.gov.tw/FJUD/data.aspx?ty=JD&id=PCDM,108,%E7%B0%A1,61,20190212,1[38] 楊景舜，〈臺灣新北地方檢察署檢察官聲請簡易判決處刑書107年度偵字第31434號〉，檢察機關公開書類查詢系統，2018年12月。參考來源：https://psue.moj.gov.tw/psiqs/print.jsp?d=9464d33adc0bca1f4353624f50cdc1e5[39] 臺灣彰化地方檢察署，〈為什麼要推動修復式司法？〉，2015年3月。參考來源：https://www.chc.moj.gov.tw/296309/296431/709708/296446/453081/[40] 薛植和，〈臺灣新北地方檢察署檢察官起訴書109年度偵字第590號〉，檢察機關公開書類查詢系統，2020年4月。參考來源：https://psue.moj.gov.tw/psiqs/print.jsp?d=6ab40b4d5b331df329a3198c061c3984 描述 碩士
國立政治大學
資訊科學系
107753027資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107753027 資料類型 thesis dc.contributor.advisor 劉昭麟 zh_TW dc.contributor.advisor Liu, Chao-Lin en_US dc.contributor.author (Authors) 李右元 zh_TW dc.contributor.author (Authors) Lee, Yu-Yuan en_US dc.creator (作者) 李右元 zh_TW dc.creator (作者) Lee, Yu-Yuan en_US dc.date (日期) 2021 en_US dc.date.accessioned 2-Mar-2021 14:32:02 (UTC+8) - dc.date.available 2-Mar-2021 14:32:02 (UTC+8) - dc.date.issued (上傳時間) 2-Mar-2021 14:32:02 (UTC+8) - dc.identifier (Other Identifiers) G0107753027 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/134084 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 107753027 zh_TW dc.description.abstract (摘要) 近年來，隨著技術的成長，自然語言處理的工作在不同的領域間發展，其中亦包含法律面向。在台灣，法律與科技的應用目前仍在起步的階段，有些社群活動亦開始著重於此面向，例如法律科技黑客松。 就台灣的刑事訴訟而言，案件會先經由檢察官的偵查，若被告遭受起訴處分，案件才會移交由法官進行審理及判決。而訴訟的過程往往曠日廢時，其潛在原因可能是被告對於判決結果的不符而上訴。此外，因應國民法官的推動，台灣可能逐步走向參審制的判決。相較於現任法官，國民法官可能沒有法律相關的知識或判決相關的經驗，使其對於最終判決的影響可能較不客觀。 因此本實驗以輔助判決為目標，其對象可以是一般民眾、被告、國民法官，甚至是現任法官及律師等。實驗結合判決結果預測以及類似案件推薦兩部分工作，除了提供使用者可能的判決結果，亦透過「與預測結果相符」及「與預測結果不符」二類相似案件，提供不同面向的案件做為比較及參考。 在過去判決結果預測的相關實驗中，多是以裁判書作為實驗語料。我們則將訴訟流程往回推一步，採用起訴書作為主要語料，希望能在判決結果確定前就對於案件提供相關輔助功能。而在起訴書數量較少，且判決類別不平均的情況下，判決結果預測的實驗目前最高平均值能達到0.665 的Macro_F1分數，在類似案件推薦的實驗中也確實能透過起訴書內容，找出類似案件。 zh_TW dc.description.abstract (摘要) With the advance of science and technology, the works of natural language processing have been growing in many different fields in recent years. In Taiwan, the applications between law and technology are still in their infancy, while some communities have begun to focus on these aspect, such as Legaltech Hackathon.In terms of criminal proceeding in Taiwan, the case will first be investigated by the prosecutor. If the defendant is charged, the case will be transferred to the judge for trial and judgement. But the judicial proceeding is usually time-consuming, and the reason may be that the prosecutor or defendant appealed against the judgement. Furthermore, with the promotions of citizen judge system, Taiwan may gradually move towards a lay judge system. Compared with professional judges, citizen judges may not have legal knowledge or judgement-related experience, which may lead to the less objective judgements.Therefore, the goal of this experiment is to assist judgements, and its objects can be defendants, citizen judges, and even professional judges and lawyers. Our experiment combines two parts of the work, which are judgement predicting and similar cases recommending. In addition to providing users with possible judgements, the two types of similar cases "consistent with the prediction" and "not consistent with the prediction" are also provided for comparison of different aspects of the case.In the past related experiments, court’s judgements were mostly used as experiment corpus. To provide relevant auxiliary functions for the case before the judgement confirmed, we use the indictments as our main corpus. However, with small amount of indictments and unbalanced judgement types, our judgement predicting can still have 0.665 of macro f-1 score, and the similar cases can indeed be found through the content of the indictment. en_US dc.description.tableofcontents 1 緒論 11.1 研究動機 11.2 研究目的 21.3 論文架構 32 文獻回顧 42.1 判決傾向性預測 42.2 判決結果預測 42.3 國民法官相關研究 53 實驗語料與系統架構 63.1 語料來源 63.2 語料整合 93.3 系統架構 104 語料前處理 114.1 語料篩選 114.2 語料清理 134.3 語料斷詞 154.4 語料分割 175 文字向量化 185.1 Word2Vec 185.2 Doc2Vec 205.3 TF-IDF 215.4 BERT 216 判決結果預測 226.1 SVM 226.2 BiLSTM 236.2.1 詞向量 296.2.2 句向量 316.2.3 使用詞向量加入集成學習 326.3 BERT 336.4 實驗評估方法 367 類似案件推薦 377.1 餘弦相似度 387.2 TS-SS相似度 388 實驗結果 408.1 判決結果預測 408.1.1 實驗圖表說明 408.1.2 SVM＋Doc2Vec 418.1.3 SVM＋TF-IDF 478.1.4 BiLSTM＋詞向量 488.1.5 BiLSTM＋Word2Vec句向量 578.1.6 BiLSTM＋一層投票 668.1.7 BiLSTM＋兩層投票 708.1.8 BiLSTM＋Doc2Vec句向量 758.1.9 BERT 838.1.10 判決結果預測實驗總評 848.2 類似案件推薦 878.2.1 餘弦相似度＋Doc2Vec 878.2.2 餘弦相似度＋TF-IDF 918.2.3 TS-SS相似度＋Doc2Vec 948.2.4 TS-SS相似度＋TF-IDF 979 結論與未來展望 1009.1 結論 1009.2 未來展望 101參考文獻 102附錄一 口試相關討論 105附錄二 口試相關實驗 1081 BiLSTM＋Word2Vec句向量 1081.1 BiLSTM＋Word2Vec句向量［，。］ 1081.2 BiLSTM＋Word2Vec句向量［，。；］ 1172 BiLSTM＋Doc2Vec句向量 1252.1 BiLSTM＋Doc2Vec句向量［，。］ 1252.2 BiLSTM＋Doc2Vec句向量［，。；］ 134 zh_TW dc.format.extent 12463618 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107753027 en_US dc.subject (關鍵詞) 判決結果預測 zh_TW dc.subject (關鍵詞) 類似案件推薦 zh_TW dc.subject (關鍵詞) 法律科技應用 zh_TW dc.subject (關鍵詞) 輔助判決 zh_TW dc.subject (關鍵詞) 深度學習 zh_TW dc.subject (關鍵詞) judgement predicting en_US dc.subject (關鍵詞) similar cases recommending en_US dc.subject (關鍵詞) the application of LegalTech en_US dc.subject (關鍵詞) judgement assistance en_US dc.subject (關鍵詞) deep learning en_US dc.title (題名) 透過起訴書輔助法院判決－以竊盜罪為例 zh_TW dc.title (題名) Using Indictments to Assist Judges in Judging – A Case Study with Offenses of Larceny en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] C. 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