Legal documents categorization by compression

Abstract

In this paper we investigate how to categorize text excerpts from Italian normative texts. Although text categorization is a problem of broader interest, we single out a specific issue. Namely, we are concerned with categorizing the set of subjects in which Italian Regions are allowed to produce norms: this is the so-called residual legislative power problem. It basically consists in making explicit a set of subjects that was originally defined only in a residual and negative fashion. The categorization of legal text fragments is acknowledged to be a difficult problem, featured by abstract concepts along with a variety of locutions used to denote them, by convoluted sentence structure, and by several other facets. In addition, in the present case subjects are often partially overlapped, and a training set of sufficient size (for the problem under consideration) does not exist: all these aspects make our task challenging. In this setting, classical feature-based approaches provide poor quality results, so we explored algorithms based on compression techniques. We tested three such techniques: we illustrate their main features and report the results of an experimentation where our implementation of such algorithms is compared with the output of standard machine learning algorithms. Far from having found a silver bullet, we show that compression-based techniques provide the best results for the problem at hand, and argue that these approaches can be effectively coupled with more informative and semantically grounded ones.

An innovative multi-segment strategy for the classification of legal judgments using the k-nearest neighbour classifier

“…When k = 1, the category of the nearest neighbour is taken to be the category of the document for which the prediction is being done. When k is larger than 1, a majority voting system is used to select the best category [6]. Weights can also be assigned to the nearest neighbours.…”