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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/144044
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/144044


    Title: 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
    Authors: 劉威志
    Liu, Wei-Zhi
    Contributors: 黃詩淳
    劉昭麟

    Huang, Sieh-Chuen
    Liu, Chao-Lin

    劉威志
    Liu, Wei-Zhi
    Keywords: 判決預測
    民事案件
    給付扶養費
    legal judgment prediction
    civil case
    the issues of alimony
    Date: 2023
    Issue Date: 2023-04-06 18:00:45 (UTC+8)
    Abstract: 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.
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    Description: 碩士
    國立政治大學
    資訊科學系
    109753157
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109753157
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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