Citation mining: integrating text mining and bibliometrics for research user profiling
Journal of the American Society for Information Science and Technology
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
The Journal of Machine Learning Research
Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Full-text and topic based authorrank and enhanced publication ranking
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
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The goal of this paper is to use innovative text and graph mining algorithms along with full-text citation analysis and topic modeling to enhance classical bibliometric analysis and publication ranking. By utilizing citation contexts extracted from a large number of full-text publications, each citation or publication is represented by a probability distribution over a set of predefined topics, where each topic is labeled by an author contributed keyword. We then used publication/citation topic distribution to generate a citation graph with vertex prior and edge transitioning probability distributions. The publication importance score for each given topic is calculated by PageRank with edge and vertex prior distributions. Based on 104 topics (labeled with keywords) and their review papers, the cited publications of each review paper are assumed as "important publications" for ranking evaluation. The result shows that full text citation and publication content prior topic distribution along with the PageRank algorithm can significantly enhance bibliometric analysis and scientific publication ranking performance for academic IR system.