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Automated paper annotation with ReaderBench

Abstract : The annotation of articles from a given domain and the generation of semantic metadata can be considered a reliable foundation for creating a paper recommender system. Within this paper, the models from other previous researches are extended with the capability of visualizing articles and the most important concepts from a domain within imposed timeframes. This can be very useful for researchers to check out the most important publications from a given period, to view which are the trends and how a domain has evolved. Our previous analyses used the articles to build a paper graph and to suggest the most relevant articles, given a user defined query in natural language. This research contains a use case and creates visual graph representations to enhance the overall perception of the evolution of a domain.
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Contributor : Philippe Dessus <>
Submitted on : Sunday, October 25, 2015 - 7:14:35 AM
Last modification on : Friday, March 27, 2020 - 10:31:11 AM
Document(s) archivé(s) le : Thursday, April 27, 2017 - 6:52:13 AM


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  • HAL Id : hal-01217024, version 1




Ionut Cristian Paraschiv, Mihai Dascalu, Ștefan Trăușan-Matu, Philippe Dessus. Automated paper annotation with ReaderBench. 12-a Conferinta Nationala de Interactiune Om-Calculator (RoCHI 2015), Sep 2015, Bucharest, Romania. pp.75-80. ⟨hal-01217024⟩