Minh Quang Nhat Pham, Minh Le Nguyen, Akira Shimazu
163 - 166
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.
The law changes for several reasons and this change is manifested in the content of legal documents which in turn makes legal document retrieval systems be less efficient in their retrieval quality. In this work, we propose an approach for measuring a user query tendency that can augment the efficiency of XML based legal document retrieval systems. This approach has been used in the change-aware legal document retrieval model presented in .
Legal quality is a manifold issue; the presented research is focusing on the structural and referential aspects. Focusing on verbs and nouns, the text is turned into graphs, opening a compressed visual view of the legal act and offers several useful features:
• information-enriched graphical representation of the law