Important citations identification with semi-supervised classification model.

Autor/inn/en	An, Xin; Sun, Xin; Xu, Shuo
Titel	Important citations identification with semi-supervised classification model.
Quelle	In: Scientometrics, (2022) 11, S.6533-6555 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-021-04212-6
Schlagwörter	Important citation; Semi-supervised learning; Self-training; Expert-labeled dataset; Author-labeled dataset
Abstract	Abstract Given that citations are not equally important, various techniques have been presented to identify important citations on the basis of supervised machine learning models. However, only a small volume of instances have been annotated manually with the labels. To make full use of unlabeled instances and promote the identification performance, the semi-supervised self-training technique is utilized here to identify important citations in this work. After six groups of features are engineered, the SVM and RF models are chosen as the base classifiers for self-training strategy. Then two experiments based on two different types of datasets are conducted. The experiment on the expert-labeled dataset from one single discipline shows that the semi-supervised versions of SVM and RF models significantly improve the performance of the conventional supervised versions when unannotated samples under 75% and 95% confidence level are rejoined to the training set, respectively. The AUC-PR and AUC-ROC of SVM model are 0.8102 and 0.9622, and those of RF model reach 0.9248 and 0.9841, which outperform their counterparts and the benchmark methods in the literature. This demonstrates the effectiveness of our semi-supervised self-training strategy for important citation identification. Another experiment on the author-labeled dataset from multiple disciplines, semi-supervised learning models can perform better than their supervised learning counterparts in term of AUC-PR when the ratio of labeled instances is less than 20%. Compared to our first experiment, insufficient amount of instances from each discipline in our second experiment enables the performance of the models to be unsatisfactory.
Erfasst von	OLC
Update	2023/2/05