Update Legal Documents Using Hierarchical Ranking Models and Word Clustering
Pham Minh Quang Nhat, Le-Minh Nguyen, Akira Shimazu · Frontiers in artificial intelligence and applications · 2010
Our research addresses the task of updating legal documents when new information emerges. In this paper, we employ a hierarchical ranking model to the task of updating legal documents. Word clustering features are incorporated to the ranking models to exploit semantic relations between words. Experimental results on legal data built from the United States Code show that the hierarchical ranking model with word clustering outperforms baseline methods using Vector Space Model, and word cluster-based features are effective features for the task.