Authors:
Dominique Mercier
1
;
2
;
Syed Tahseen Raza Rizvi
1
;
2
;
Vikas Rajashekar
2
;
Andreas Dengel
1
;
2
and
Sheraz Ahmed
1
Affiliations:
1
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
;
2
Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany
Keyword(s):
Deep Learning, Natural Language Processing, Intent Classification, Sentiment Classification, Document Processing.
Abstract:
Citations play a vital role in understanding the impact of scientific literature. Generally, citations are analyzed
quantitatively whereas qualitative analysis of citations can reveal deeper insights into the impact of a scientific
artifact in the community. Therefore, citation impact analysis including sentiment and intent classification
enables us to quantify the quality of the citations which can eventually assist us in the estimation of ranking and
impact. The contribution of this paper is three-fold. First, we provide ImpactCite, which is an XLNet-based
method for citation impact analysis. Second, we propose a clean and reliable dataset for citation sentiment
analysis. Third, we benchmark the well-known language models like BERT and ALBERT along with our
proposed approach for both tasks of sentiment and intent classification. All evaluations are performed on a
set of publicly available citation analysis datasets. Evaluation results reveal that ImpactCite achieves a new
state-of-
the-art performance for both citation intent and sentiment classification by outperforming the existing
approaches by 3.44% and 1.33% in F1-score. Therefore, the evaluation results suggest that ImpactCite is a
single solution for both sentiment and intent analysis to better understand the impact of a citation.
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