Classification and clustering for case-based criminal summary judgments

  • Authors:

  • Chao-Lin Liu;Cheng-Tsung Chang;Jim-How Ho

  • Affiliations:

  • National Chengchi University, Taipei, Taiwan;National Chengchi University, Taipei, Taiwan;Panchiao District Court, Taipei, Taiwan

  • Venue:
  • ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
  • Year:
  • 2003

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Abstract

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.