NERank: Ranking Named Entities in Document Collections

Chengyu Wang, Rong Zhang, Xiaofeng He, Aoying Zhou

Abstract: While most of the entity ranking research focuses on Web corpora with user queries as input, little has been done to rank entities directly from documents. We propose a ranking algorithm NERank to address this issue. NERank employs a random walk process on a weighted tripartite graph mined from the document collection. We evaluate NERank over real-life document datasets and compare it with baselines. Experimental results show the effectiveness of our method.