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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/147028
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/147028

    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
    Lin, Hong-Ren
    Keywords: 自然語言處理
    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


    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。


    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
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109753156
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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