Predicting associated statutes for legal problems |
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| Authors: | Yi-Hung Liu Yen-Liang Chen Wu-Liang Ho |
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| Institution: | 1. Department of Information Management, National Central University, Chung-Li 320, Taiwan, ROC;2. Department of Legal Service, Straits Exchange Foundation, Taipei 105, Taiwan, ROC |
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| Abstract: | Applying text mining techniques to legal issues has been an emerging research topic in recent years. Although a few previous studies focused on assisting professionals in the retrieval of related legal documents, to our knowledge, no previous studies could provide relevant statutes to the general public using problem statements. In this work, we design a text mining based method, the three-phase prediction (TPP) algorithm, which allows the general public to use everyday vocabulary to describe their problems and find pertinent statutes for their cases. The experimental results indicate that our approach can help the general public, who are not familiar with professional legal terms, to acquire relevant statutes more accurately and effectively. |
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| Keywords: | Text mining Statute Criminal judgment Normalized Google Distance (NGD) Support vector machines (SVM) Apriori algorithm |
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