LEXA: Building knowledge bases for automatic legal citation classification

• Legal citation classification system using knowledge acquisition.
• Rule language based on regular expressions of annotations.
• Facilitate the acquisition and maintenance of rules leveraging the available corpus.
• The system outperforms machine learning classifiers on different datasets.

This paper presents a new approach to building legal citation classification systems. Our approach is based on Ripple-down Rules (RDR), an efficient knowledge acquisition methodology. The main contributions of the paper (over existing expert-systems approaches) are extensions to the traditional RDR approach introducing new automatic methods to assist in the creation of rules: using the available dataset to provide performance estimates and relevant examples, automatically suggesting and validating synonyms, re-using exceptions in different portions of the knowledge base. We compare our system LEXA with baseline machine learning techniques. LEXA obtains better results both in clean and noisy subsets of our corpus. Compared to machine learning approaches, LEXA also has other advantages such as supporting continuous extension of the rule base, and the opportunity to proceed without an annotated data set and to validate class labels while building rules.