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

Exploiting Machine Learning Models for Chinese Legal Documents Labeling, Case Classification, and Sentencing Prediction

林琬真(Wan-Chen Lin) ； 郭宗廷(Tsung-Ting Kuo) ； 張桐嘉(Tung-Jia Chang) ； 顏厥安(Chueh-An Yen) ； 陳昭如(Chao-Ju Chen) ； 林守德(Shou-De Lin)

《中文計算語言學期刊》 17卷4期 (2012/12) Pp. 49-67

https://doi.org/10.30019/IJCLCLP.201212.0004

摘要

人工智慧於法學領域所發展出的法學資訊系統，主要提供參考資訊以協助司法審判；其重要議題包括法律文件分類、法律文件摘要、類似過去案例搜尋、協助判刑等。本論文著重探討「強盜罪」與「恐嚇取財罪」的分類，以及此兩種罪的量刑預測。我們針對「強盜罪」與「恐嚇取財罪」來定義21種法律要素標籤，並嘗試自動擷取所定義之標籤。實驗說明利用我們定義之法律要素標籤，能確實改善案件分類，以及進行量刑預測。最後，我們亦針對實驗結果，討論「強盜罪」與「恐嚇取財罪」的特徵，以及影響判刑長短之因素。

關鍵字

法律文件分類 ； 量刑預測 ； 強盜罪 ； 恐嚇取財罪

並列摘要

This paper exploits machine learning methods to separate robbery and intimidation cases, and predicting their sentencing by considering defined legal factors. We introduce a framework to fetch 21 legal factor labels of robbery and intimidation cases, then use the labels for case classification and sentencing prediction. Our experiments show that the legal factor labels can indeed improve the results of case classification and sentencing prediction. We then discuss the influence of these legal factors in both case classification and sentencing prediction tasks.

並列關鍵字

Case Classification ； Sentencing Prediction ； Robbery ； Intimidation

參考文獻

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