Legal information as a complex network: Improving topic modeling through homophily
Abstract
Topic modeling is a key component to computational legal science. Network analysis is also very important to further understand the structure of references in legal documents. In this paper, we improve topic modeling for legal case documents by using homophily networks derived from two families of references: prior cases and statute laws. We perform a detailed analysis on a rich legal case dataset in order to create these networks. The use of the reference-induced homophily topic modeling improves on prior methods.
Chenhui Chu
Guest Associate Professor
Benjamin Renoust
Guest Associate Professor
Noriko Takemura
Guest Associate Professor
She is working on ambient intelligence and gait recognition using pattern recognition and machine learning.
Yuta Nakashima
Associate Professor
Yuta Nakashima is an associate professor with Institute for Datability Science, Osaka University. His research interests include computer vision, pattern recognition, natural langauge processing, and their applications.
Hajime Nagahara
Professor
He is working on computer vision and pattern recognition. His main research interests lie in image/video recognition and understanding, as well as applications of natural language processing techniques.