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Unpublished Paper
Mining a Digital Library for Influential Authors
(2007)
Abstract
When browsing a digital library of research papers, it is natural to ask which authors are most influential in a particular topic. We present a probabilistic model that ranks authors based on their influence in particular areas of scientific research. This model combines several sources of information: citation information between documents as represented by PageRank scores, authorship data gathered through automatic information extraction, and the words in paper abstracts. We propose a topic model on the words, and compare performance versus a smoothed language model by assessing the number of major award winners in the resulting ranked list of researchers.
Keywords
- Expert Retrieval,
- Information Systems,
- Digital Libraries
Disciplines
Publication Date
2007
Comments
This is the pre-published version harvested from CIIR.
Citation Information
David Mimno and Andrew McCallum. "Mining a Digital Library for Influential Authors" (2007) Available at: http://works.bepress.com/andrew_mccallum/113/