Generalizing Hierarchical Structure of Indices for Japanese Legal Documents

The idea of hierarchical index is applied to the legal domain to provide the readers a general understanding of legal concepts via their super/sub-ordinate relations. This work serves as effort in automatic legal ontology learning in which super/sub-ordinate relations are considered. Indices are extracted from legal documents as keywords and their relationships are discovered by language processing method. We propose an approach to extract the super/sub- ordinate relation between each pair of concepts individually based on directional similarity. The relations among a set of legal indices are represented in a directed graph and the hierarchical structure of indices is simply exported from this graph. We adopt this proposal to the Japanese National Pension Act document. The resulted hierarchical structure is compared to an annotated legal ontology on the number of correct relations. The proposed method achieves 40.6% for precision, 46.9% for recall and 43.5% for F-measure as the performance.