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Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004)
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- Date
- 31. 5.2004 19:22:06
- Language
- e
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Wu, H.; He, J.; Pei, Y.: Scientific impact at the topic level : a case study in computational linguistics (2010)
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- 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.
- Language
- e
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Wu, H.; Salton, G.: ¬The estimation of term relevance weights using relevance feedback (1981)
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Studwell, W.E.; Wang, R.; Wu, H.: ¬A tale of two decades : the controversy over the choice of a Chinese language romanization system in American cataloging practice (1993)
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Studwell, W.E.; Wang, R.; Wu, H.: Ideological influences on book classification schemes in the People's Republic of China (1994)
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Radev, D.; Fan, W.; Qu, H.; Wu, H.; Grewal, A.: Probabilistic question answering on the Web (2005)
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Li, H.; Wu, H.; Li, D.; Lin, S.; Su, Z.; Luo, X.: PSI: A probabilistic semantic interpretable framework for fine-grained image ranking (2018)
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Ning, X.; Jin, H.; Wu, H.: RSS: a framework enabling ranked search on the semantic web (2008)
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Hu, K.; Luo, Q.; Qi, K.; Yang, S.; Mao, J.; Fu, X.; Zheng, J.; Wu, H.; Guo, Y.; Zhu, Q.: Understanding the topic evolution of scientific literatures like an evolving city : using Google Word2Vec model and spatial autocorrelation analysis (2019)
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