政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/144044

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/144044

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 113656/144643 (79%)
造访人次 : 51747856 在线人数 : 581

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜寻范围

查询小技巧：

您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果

若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据

进阶搜寻

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/144044

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/144044

题名:	老年扶養費請求案件之准駁及扶養金額預測 Predicting Judgments and Grants for Civil Cases of Alimony for the Elderly
作者:	劉威志 Liu, Wei-Zhi
贡献者:	黃詩淳劉昭麟 Huang, Sieh-Chuen Liu, Chao-Lin 劉威志 Liu, Wei-Zhi
关键词:	判決預測民事案件給付扶養費 legal judgment prediction civil case the issues of alimony
日期:	2023
上传时间:	2023-04-06 18:00:45 (UTC+8)
摘要:	有鑒於近年請求扶養費民事訴訟案件有上升之趨勢，考慮伴隨著調解件數增長，未來量能或將難以負荷。而根據法律扶助基金會的年度報告顯示，民事訴訟法律扶助案件中家事案件數量最多的案由為給付扶養費案件，因此本實驗針對其案件進行研究並提出扶養費准駁預測模型與扶養金額預測模型。本實驗以輔助判決為目標，其對象可以是一般民眾、聲請人亦即原告、相對人亦即被告、律師或是法官，對於一般民眾可以透過此模型來了解自己是否能夠獲得其應有的扶養費；對於聲請人與相對人而言則主要是希望兩造都能接受一個共識的扶養金額並多少減輕法官的負擔；對於法官而言則是希望能提供一個客觀數據來參考，並能快速給予公平的裁判。本研究對兩造主張段落進行斷詞、模糊化及向量化，並以機器學習及深度學習為基礎建立二分類模型來進行扶養費的准駁預測，而本實驗會對有無模糊化之兩造主張段落進行向量化與模型預測搭配分別有 TF-IDF 搭配 naïve-Bayes 與 logistic regression 和 SBERT 搭配單純的平均句向量與 BiLSTM 串接並以深度學習方式來訓練與預測，故共計八種方式進行評估 accuracy、precision、recall、F1 score。本實驗也提出對有限且客觀特徵值使用 model tree 來建構迴歸模型進行扶養金額預測，並比較未使用model tree 單純用 linear regression 、使用 model tree 且各分支皆為 linear regression 和使用 model tree 且各分支使用不同的預測模型之三者的 MAE，同時為了節省人工標註特徵值需要大量的人力與時間，讓機器能全自動化進行標註與訓練模型，本實驗有透過 W2NER 來進行特徵值的提取，並進行後續模型的扶養金額預測模型訓練。扶養費准駁預測的實驗中以 logistic regression 的表現最佳其平均的分數為 0.715，而扶養金額預測的平均 MAE 為 1992.88，然而透過 W2NER 進行自動提取特徵值並搭配後續金額預測模型其平均 MAE 則為 3912.93。透過本實驗模型後望提供未來請求扶養費案件中之兩造乃至法官一客觀參考基準，以期未來能在庭外調解時提供一相對客觀的試算結果供有扶養費爭議之兩造參考並儘早達成共識，亦或給予法官參考數據輔助以期能加速判決之進程，進而減少司法資源的浪費。 The needs for mediation are increasing rapidly along with the increasing number of cases of the alimony for the elderly in recent years. According to Legal Aid Foundation’s annual report, cases of the alimony for the elderly has account for the largest number in the foundation’s civil cases. Therefore, this research focus particularly on these cases, offering a prediction mechanism for predicting the outcomes of some prospective lawsuits may alleviate the workload of the mediation courts. This research aims to offer predictions for the judgments and the granted alimony for the plaintiffs of the alimony for the elderly cases in Chinese. For the general public, the predictions can be used to understand whether they can get the granted alimony; for the plaintiff and defendant, it could be a reference for both parties to reach an early consensus; for the judges, it could be an objective data and hopefully speed up the judicial process. To build the current binary classification system for judgments predictions, we segment, blur and vectorize the texts of the judgement documents of the past lawsuits. In the experiment, we vectorize both blur and non-blur documents and train the model using TF-IDF with naïve-Bayes and logistic regression, SBERT with average embedding, SBERT with BiLSTM, total 8 method to train and evaluate the accuracy, precision, recall and F1 score. For the granted alimony predictions, we apply model tree for predicting the judgments and compare with applying only linear regression, model tree with branches using linear regression and model tree with branches using different predicting model for MAE score. Furthermore, we use W2NER to help on feature extraction, saving great amount of manual labeling time. In our experiment, logistic regression has the best performance for judgments predictions and the average score for predicting the judgments is 0.715. For the granted alimony predictions, model tree with branches using different predicting model has the best performance with the average MAE score 1992.88, and the average MAE score via W2NER to perform feature extraction is 3912.93. We hope the results can provide an objective reference for the involved parties to reach an early consensus and provide as supportive data for the courts in order to speed up the process of judgment.
參考文獻:	藍家樑，中文訴訟文書檢索系統雛形實作，國立政治大學資訊科學系碩士論文，2009。曹錫璋，基於深度學習模型之判決書情境相似檢索技術之研究，國立中興大學資訊科學與工程學系所碩士論文, 2021. 何君豪，階層式分群法在民事裁判要旨分群上之應用，國立政治大學資訊科學系碩士論文, 2007. 林琬真，機器學習於中文法律文件之標記與分類，中文計算語言學期刊第 17 卷 4 期第49 - 68頁，2012。李右元，透過起訴書輔助法院判決-以竊盜罪為例，國立政治大學資訊科學系碩士論文，2021。何君豪，AI 引入民事程序可行性之研究，國立臺灣科技大學資訊管理系博士論文，2021。黃詩淳、邵軒磊，以人工智慧讀取親權酌定裁判文本: 自然語言與文字探勘之實踐，臺大法學論叢 NTU Law Journal 第 49 卷 1 期第195 - 224頁，2020。 Zellig S. Harris. Distributional Structure, WORD, Volume: 10, no. 2-3, Pages: 146-162, 1954. Available: https://doi.org/10.1080/00437956.1954.11659520 Fabrice Muhlenbach, Long Nguyen Phuoc and Isabelle Sayn. Predicting Court Decisions for Alimony: Avoiding Extra-legal Factors in Decision made by Judges and Not Understandable AI Models. 2020. Available: arXiv:2007.04824 黃詩淳，老親扶養費酌定裁判之實證研究，台灣大學數位智能法院、法律科技與接近正義研討會，2022。 Donato Malerba, Floriana Esposito, Michelangelo Ceci and Annalisa Appice. Top-down induction of model trees with regression and splitting nodes. IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume: 26, Issue: 5, Pages: 612-625. 2004. 林玠鋒，論家事財產法上法院之裁量調控-以扶養費、家庭生活費用及贍養費之酌付為中心，國立政治大學法律學系所博士論文，2014。謝天懷、賴俊穎、黃詩淳，老人扶養費請求事件之實證研究，裁判時報，第 115 期第84 - 95頁，2022。Available: https://doi.org/10.53106/207798362022010115008 林岡毅，以資訊技術分析我國離婚贍養費相關裁判，國立臺灣大學科際整合法律學研究所碩士論文，2018。陳冠群，中文裁判書之要旨擷取：以最高法院裁判書為例，國立政治大學碩士論文，2018。 P.-H. Li, T.-J. Fu, and W.-Y. Ma, Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER, Proceedings of AAAI Conference on Artificial Intelligence, Volume: 34, no. 5. 2019. Gerard Salton and Chris Buckley. Term Weighting Approaches in Automatic Text Retrieval, Information Processing & Management, Volume: 24, Issue: 5, Pages: 513-523. 1988. Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. 2018. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. Attention Is All You Need. 2017. N. Reimers and I. Gurevych. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. 2019. Jingye Li, Hao Fei, Jiang Liu, Shengqiong Wu, Meishan Zhang, Chong Teng, Donghong Ji, Fei Li, Unified Named Entity Recognition as Word-Word Relation Classification, Proceedings of the 36th AAAI Conference on Artificial Intelligence, Volume: 36, no. 10., 2022. Available: https://doi.org/10.48550/arXiv.2112.10070
描述:	碩士國立政治大學資訊科學系 109753157
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0109753157
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

文件中的档案:

档案	描述	大小	格式	浏览次数
315701.pdf		4989Kb	Adobe PDF2	0	检视/开启

在政大典藏中所有的数据项都受到原著作权保护.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回馈