我們以機器學習(Machine Learning)的方法，建立rule-based與case-based的instances，再藉由這些 instances來判斷起訴書的案由和法條，其最好的正確率只比人工建立的rules與cases所判斷的結果低7%而已。由於在我們最基本的方法中，一個判例就會被建立成一個instance，如此，我們將需要大量的空間來儲存instances，針對這個問題，我們也提出了instances clustering與刪除部份較不重要詞這兩個方法，來降低instances所佔的空間，經過簡化的系統的正確率不但與原本未刪減instances時差不多，還可以減少將近一半左右的儲存空間；而且如果我們將這兩個刪減instances的方法混合使用，甚致可以找到一個更好的解，不但能些微提升正確率，還可以把儲存instances所需的空間，降低為原本的四分之一左右。 / I apply machine learning techniques to constructing rule-based and case-based reasoning systems. These systems determine the prosecution reasons and applicable articles of lawsuits, and may achieve an accuracy that is just 7% lower than that achieved by a manually-built system. The baseline method constructs one instance for each prior lawsuit, so it takes much space to store all instances. To reduce the storage space, I propose two methods – clustering instance and removing some less important words in instances. The effects of these methods not only maintain the original accuracy, but also reduce the storage space by half. When I integrated all proposed methods, I can even improve the accuracy slightly and reduce the storage space by three quarters.
| Identifer | oai:union.ndltd.org:CHENGCHI/G0090753005 |
| Creators | 張正宗, Cheng-Tsung Chang |
| Publisher | 國立政治大學 |
| Source Sets | National Chengchi University Libraries |
| Language | 中文 |
| Detected Language | English |
| Type | text |
| Rights | Copyright © nccu library on behalf of the copyright holders |
Page generated in 0.002 seconds