@inproceedings{2c0a5bc051084c209fa66fc3ed88cf43,
title = "NERank: Bringing order to named entities from texts",
abstract = "Most entity ranking research aims to retrieve a ranked list of entities from a Web corpus given a query. However, entities in plain documents can be ranked directly based on their relative importance, in order to support entity-oriented Web applications. In this paper, we introduce an entity ranking algorithm NERank to address this issue. NERank first constructs a graph model called Topical Tripartite Graph from a document collection. A ranking function is designed to compute the prior ranks of topics based on three quality metrics. We further propose a meta-path constrained random walk method to propagate prior topic ranks to entities. We evaluate NERank over real-life datasets and compare it with baselines. Experimental results illustrate the effectiveness of our approach.",
keywords = "Entity ranking, Meta-path constrained random walk, Topic modeling, Topical tripartite graph",
author = "Chengyu Wang and Rong Zhang and Xiaofeng He and Guomin Zhou and Aoying Zhou",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 18th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2016 ; Conference date: 23-09-2016 Through 25-09-2016",
year = "2016",
doi = "10.1007/978-3-319-45814-4\_2",
language = "英语",
isbn = "9783319458137",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "15--27",
editor = "Guanfeng Liu and Feifei Li and Kyuseok Shim and Kai Zheng",
booktitle = "Web Technologies and Applications - 18th Asia-Pacific Web Conference, APWeb 2016, Proceedings",
address = "德国",
}