Automatic categorization of case law
Paul Thompson · 2001
This paper describes a series of automatic text categorization experiments with case law documents. Cases are categorized into 40 broad, high-level categories. These results are compared to an existing operational process using Boolean queries manually constructed by domain experts. In this categorization process recall is considered more important than precision. This paper investigates three algorithms that potentially could automate this categorization process: 1) a nearest neighbor-like algorithm, 2) C4.5rules, a machine learning decision tree algorithm; and 3) Ripper, a machine learning rule induction algorithm. The results obtained by Ripper surpass those of the operational process.