| Summary: | 碩士 === 國立成功大學 === 經濟學系 === 107 === The purpose of this study was to establish an effective classification to find out the difference between Capital punishment and Life imprisonment. Establishing features by difference and assisting judge’s adjudicate, thereby reducing the cost of trial to execution of criminal.
At present, domestic research on the classification of legal documents was quite complete, it was mostly to classify cases by using the characteristic words and SVM. But, it was seldom used to classify Chinese judgments because the processing method of Chinese was different from English, there were many difficulties in marking its part of speech. Due to the above difficulties, bring about the legal judgment analysis required a lot of manpower to process, which made the research difficult. This study make use of the government data open platform to find out the legal judgement of Capital punishment and Life imprisonment. And use it as training data to train word2vec to establish semantic space. After obtaining the decision vector, utilize K-medians and SVM to classify it. However, during the experiment, it was found that there was too much meaningless information in the judgment, which influence the accuracy rate. Ultimately, the main information on the judgment was based on the contents of the find and establishment of the Court , and merged the negative words as training materials for word2vec. Taking rate words as characteristic words, and extract the more representative features of the judgment, and classify it.
Found from empirical results, the death penalty and the life imprisonment, were also as expected, were very similar. However, by using the characteristic words, the differencebetween the death penalty and the life imprisonment was established, so that the two can be clearly distinguished.
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