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| Title: | 利用詞組檢索中文訴訟文書之研究
An Exploration of Indexing Chinese Judicial Documents with Term Pairs |
| Authors: | 謝淳達
Hsieh,Chwen-Dar |
| Contributors: | 劉昭麟
Liu,Chao-Lin
謝淳達
Hsieh,Chwen-Dar |
| Keywords: | 法學資訊
自然語言處理
Machine Learning |
| Date: | 2004 |
| Issue Date: | 2009-09-17 13:55:29 (UTC+8) |
| Abstract: | 本文將針對相似訴訟文書之搜尋進行研究與探討。在這裡所說的「相似案件」指的是有著相同犯罪行為的案件。判例是法院對於訴訟案件所作的確定判決的先例。在法律案件審判的過程中，對法官和律師而言，與目前的新案件案情相似的過去判例有時是有參考價值的。這意味著我們可以透過判例來推測新的訴訟案件可能的判決方向，因此搜尋過去判例是有其價值的。與一般常用的資訊檢索方法中以單一詞彙作為索引不同的是，我們嘗試以案件事實段中的詞組（兩個詞彙的組合）集合為基礎，由於詞組所包含的資訊比詞彙還多，我們希望透過詞組集合的比對，能夠更精確地找出類似於新案件的過去判例，藉此幫助一般人搜尋過去的相似判例，並能夠從過去判例中自行推測所遇上的法律糾紛可能的判決方向。然而，由於既有的電子詞典並未包含所有可能的詞彙，尤其是訴訟文件中常出現的一些特定詞彙，因此我們提出了一個可以從文件中自動擷取可能的中文詞彙的方法，並且利用這些擷取而得的詞彙協助我們分析判決書的事實段文字。此外我們將相似案件搜尋系統應用在實作「案件分類器」上，用以猜測新案件可能的案件類型。在我們的實驗中，我們提出的中文詞彙擷取方法TermSpotter所擷取出來的詞彙中，詞頻為30次以上的擷取正確率（人工判定為有用的詞彙數量╱程式輸出詞彙數量）為56.3%，而且這些詞彙經過人工過濾後，有三分之一的詞彙（953個）是HowNet電子詞典中所沒有的詞彙。而我們實作的案件分類器，在竊盜、搶奪、強盜、贓物、恐嚇、傷害、賭博七大類型案件的案由分類實驗有89.3%的正確率，而賭博罪的法條分類實驗也有81.9%的正確率。至於相似案件搜尋實驗中，我們以人工判斷其效果，目前所搜尋到的過去判例只有42%是值得參考的，未來仍有空間需要繼續嘗試改進。
I study information retrieval methods for retrieving similar judicial documents. Here “similar judicial documents” refers to “cases that have a similar process of criminal violation”. For judges and lawyers, it is sometimes worth referring to prior cases which are similar to the new case in the process of judgment. Information about the judgments of the similar prior cases helps people to obtain a rough picture about how the new cases might be judged. In this work, I use phrases, rather than individual words as indices of Chinese judicial documents. Phrases provide a better foundation for indexing and retrieving documents than individual words. Constituents of phrases make other component words in the phrase less unambiguous than when the words appear separately. I expect the system could help anyone who is not a legal expert to retrieve similar prior cases on their own.
The existing electronic dictionary does not collect all the possible words, especially the words that appear in specific-domain documents. Hence, I put forth an algorithm to automatically retrieve possible words in the corpus, and we will use these words as the basis to construct phrases in our system. Moreover, I implement the case classifier to automatically classify new cases into several different prosecution categories.
I put forth the algorithm “TermSpotter” to automatically retrieve possible words that occur more than 30 times. In the experiments, 56.3% of the retrieved words are considered as useful words after manual filtration. Among these useful words, about one third of the words are not included in HowNet, and some of them are legal-domain-specific words. The implemented case classifier categorizes new cases into seven different prosecution categories: larceny, robbery, robbery by threatening or disabling the victims, receiving stolen property, causing bodily harm, intimidation, and gambling. It reaches 89.3% in accuracy. The classifier can also categorize cases based on what criminal articles are violated. In the experiment of classifying gambling cases into four combinations of three articles, it reaches 81.9% in accuracy. In the experiment of retrieving prior cases which are similar to the new case, it only reaches 42% in accuracy judged by a practicing judge, so there is a lot of work to do to improve the classifier. |
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22. HowNet電子詞典http://www.keenage.com/
23. 台灣司法院法學資料查詢系統http://nwjirs.judicial.gov.tw/FJUD/index.htm
24. 大陸法律法規庫http://search.law.com.cn:8080/
日本裁判所 http://www.courts.go.jp/index.htm
美國最高法院判例等法律文件查詢 http://www.law.cornell.edu/index.html |
| Description: | 碩士
國立政治大學
資訊科學學系
92753008
93 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0092753008 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系] 學位論文
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