Please use this identifier to cite or link to this item:
|Title:||Identifying Important Citations Using Contextual Information from Full Text|
|Authors:||Saeed Ul Hassan|
Information Technology University
|Citation:||Proceedings of the ACM/IEEE Joint Conference on Digital Libraries. (2017)|
|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.|
|Appears in Collections:||Scopus 2016-2017|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.