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dc.contributor.authorSaeed Ul Hassanen_US
dc.contributor.authorAnam Akramen_US
dc.contributor.authorPeter Haddawyen_US
dc.contributor.otherInformation Technology Universityen_US
dc.contributor.otherMahidol Universityen_US
dc.identifier.citationProceedings of the ACM/IEEE Joint Conference on Digital Libraries. (2017)en_US
dc.description.abstract© 2017 IEEE. In this paper we address the problem of classifying cited work into important and non-important to the developments presented in a research publication. This task is vital for the algorithmic techniques that detect and follow emerging research topics and to qualitatively measure the impact of publications in increasingly growing scholarly big data. We consider cited work as important to a publication if that work is used or extended in some way. If a reference is cited as background work or for the purpose of comparing results, the cited work is considered to be non-important. By employing five classification techniques (Support Vector Machine, Naïve Bayes, Decision Tree, K-Nearest Neighbors and Random Forest) on an annotated dataset of 465 citations, we explore the effectiveness of eight previously published features and six novel features (including context based, cue words based and textual based). Within this set, our new features are among the best performing. Using the Random Forest classifier we achieve an overall classification accuracy of 0.91 AUC.en_US
dc.rightsMahidol Universityen_US
dc.titleIdentifying Important Citations Using Contextual Information from Full Texten_US
dc.typeConference Paperen_US
Appears in Collections:Scopus 2016-2017

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