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    Title: 以機器學習與規則方法辨識中文民事裁判書結構與爭點 : 以給付扶養費為例
    Using Machine Learning and Pattern-Based Methods for Identifying Elements and Issues in Chinese Judgment Documents of Civil Cases : A Case Study with Alimony Cases
    Authors: 林泓任
    Lin, Hong-Ren
    Contributors: 劉昭麟
    Liu, Chao-Lin
    林泓任
    Lin, Hong-Ren
    Keywords: 自然語言處理
    機器學習
    深度學習
    法學資訊
    Natural language pocessing
    Machine learning
    Deep learning
    Legal information
    Date: 2023
    Issue Date: 2023-09-01 15:23:33 (UTC+8)
    Abstract: 因應近年來給付扶養費案件在家事事件案件中比例上升,為應用於後續法
    學資訊、社會科學等領域研究及方便一般民眾閱讀並擷取裁判書中重點,在此我們選擇案由為給付扶養費案之裁判書作為語料並進行文字探勘與分析。

    裁判書中除了案件基本資訊也就是給付扶養費案件中的聲請人所提出的主
    張及相對人所提出的反駁或主張外,還包含有法院對此次案件的判決與法官如何做出判決的說明。因此本研究透過機器學習技術從裁判書的內文中找出為「聲請人主張」、「相對人主張」以及法院對該次案件的見解,也就是「案件涵攝」,還有該次案件所引用的法條及說明,也就是「法律見解」。

    為了找出前述四種裁判書的主要內容分類,本實驗將其作為一種分類問
    題,分別以段落及句子為單位進行分類。而在分類器上會分別嘗試使用傳統機器學習模型,此外也使用近年深度學習熱門的 BERT 模型或是將其作為一嵌入層(embedding layer)並接上其他深度學習模型的架構來對裁判書內容進行分類,而這四分類最終在使用深度學習模型後達到 0.816 的 F1 score。

    除了「聲請人主張」、「相對人主張」、「法律見解」、「案件涵攝」四大類別的內容外,裁判書中還紀錄另一重要資訊,也就是案件中的「爭點」,其為該次案件中訴訟雙方所提出之主張或法律上的爭執點。初步建構成前述四大類別的分類器後,本研究會進一步找出爭點。此類問題在本研究中會分別轉化為以句為單位的分類問題,或視為一種文句生成問題。

    在最後將前述四分類及爭點句的分類模型應用在裁判書判決准駁預測上,
    透過比較及分析是否增加前述分類器提供的資訊對於裁判書判決預測的準確
    度,而提供爭點及相比起沒有提供爭點的裁判預測模型,準確率自等同亂猜的0.623 提升到 0.877。
    To response to the ratio of alimony in foundation’s civil cases is increasing in recent year. It need to apply to legal, social science and other fields and make people easily to read judgement documents. We choice the case of alimony in civil cases as our corpus and retrieve the information and analysis it.

    In judgment documents, it contains not only foundation information of case but also contains the opinions of the courts, and uses of laws to reach the final decisions.
    In this research, we use machine learning technique to find the pleadings of the applicants, the responses of the opposite parties, opinions of the courts and uses of laws to reach the final decisions.

    In order to find four categories, it be seemed as a classification problem. We use counts and short sentences as instances, and try to classify it by traditional machine learning model and the popular deep learning model – BERT or use BERT as embedding layers and connect other deep learning model. At last, the classification model for four categories can reach 0.816 of F1 score.

    Except the four categories claimed in previous count, It also has an important information in judgment document – issues. Issues means a point disputed by parties to a lawsuit. This research will also try to find issues, and takes it as five categories classification problem and sentences generation problem.

    Finally, the classification model of four categories and issues will be used in judgment prediction. We compare the judgment prediction model with only information mentioned by parties and privies and model with information mentioned by parties and privies and issues. In previous one, it only can 0.623 accuracy and the later can reach 0.877 accuracy.
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    Description: 碩士
    國立政治大學
    資訊科學系
    109753156
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109753156
    Data Type: thesis
    Appears in Collections:[Department of Computer Science ] Theses

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