Classification and clustering for case-based criminal summary judgments

We investigate the effectiveness of machine-generated criteria for classification problems related to criminal summary judgments. Our system utilizes documents of closed lawsuits as training data for generating keyword-based and case-based classification criteria, and applies these machine-generated criteria for the classification tasks. To construct databases of the classification criteria, we employ different levels of lexical knowledge in extracting information from legal documents in Chinese, and build a case instance for each closed lawsuit. Experimental results indicate that case-based classification outperforms keyword-based classification, and that machine-generated cases may offer performance accuracy that is about 7% below that of human-provided cases. Hoping to boost inference efficiency of our classifiers, we also design methods that merge the machine-generated criteria. Empirical results show that our methods can maintain the classification quality within 20% of the quality achieved by human-provided cases, even when we aggressively reduce the number of previously machine-generated cases by about seventy percents.