Wu, H; He, J; Pei, YJ. 2010. Scientific Impact at the Topic Level: A Case Study in Computational Linguistics. JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY 61 (11): 2274-2287

[Eugene Garfield]

Wu, H; He, J; Pei, YJ. 2010. Scientific Impact at the Topic Level: A Case Study 
in Computational Linguistics. JOURNAL OF THE AMERICAN SOCIETY FOR 
INFORMATION SCIENCE AND TECHNOLOGY 61 (11): 2274-2287.

Author Full Name(s): Wu, Hao; He, Jun; Pei, Yijian
Language: English
Document Type: Article
KeyWords Plus: CITATION ANALYSIS; H-INDEX; WEB SEARCH; ALGORITHM; 
PAGERANK; PUBLICATION; NETWORKS; JOURNALS; OUTPUT

Abstract: In this article, we propose to apply the topic model and topic-level 
eigenfactor (TEF) algorithm to assess the relative importance of academic 
entities including articles, authors, journals, and conferences. Scientific impact 
is measured by the biased PageRank score toward topics created by the latent 
topic model. The TEF metric considers the impact of an academic entity in 
multiple granular views as well as in a global view. Experiments on a 
computational linguistics corpus show that the method is a useful and promising 
measure to assess scientific impact.

Addresses: [Wu, Hao; Pei, Yijian] Yunnan Univ, Sch Informat Sci & Engn, 
Kunming 650091, Peoples R China; [He, Jun] Nanjing Univ Informat Sci & 
Technol, Sch Elect & Informat Engn, Nanjing 210044, Peoples R China

Reprint Address: Wu, H, Yunnan Univ, Sch Informat Sci & Engn, 2 N Green Lake 
Rd, Kunming 650091, Peoples R China.

E-mail Address: haowu at ynu.edu.cn; hejun.zz at gmail.com; pei3p at ynu.edu.cn
ISSN: 1532-2882
DOI: 10.1002/asi.21396
fulltext: http://onlinelibrary.wiley.com/doi/10.1002/asi.21396/abstract