期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
NERank+: a graph-based approach for entity ranking in document collections 被引量:1
1
作者 Chengyu WANG Guomin ZHOU +1 位作者 Xiaofeng HE Aoying ZHOU 《Frontiers of Computer Science》 SCIE EI CSCD 2018年第3期504-517,共14页
Most entity ranking research aims to retrieve a ranked list of entities from a Web corpus given a user query. The rank order of entities is determined by the relevance between the query and contexts of entities. Howev... Most entity ranking research aims to retrieve a ranked list of entities from a Web corpus given a user query. The rank order of entities is determined by the relevance between the query and contexts of entities. However, entities can be ranked directly based on their relative importance in a document collection, independent of any queries. In this paper, we introduce an entity ranking algorithm named NERank+. Given a document collection, NERank+ first constructs a graph model called Topical Tripartite Graph, consisting of document, topic and entity nodes. We design separate ranking functions to compute the prior ranks of entities and topics, respectively. A meta-path constrained random walk algorithm is proposed to propagate prior entity and topic ranks based on the graph model. We evaluate NERank+ over real-life datasets and compare it with baselines. Experimental results illustrate the effectiveness of our approach. 展开更多
关键词 entity ranking Topical Tripartite Graph priorrank estimation meta-path constrained random walk
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部