Towards employing native information in citation function classification
Date
2022
Authors
Zhang, Y.
Zhao, R.
Wang, Y.
Chen, H.
Mahmood, A.
Zaib, M.
Zhang, W.E.
Sheng, Q.Z.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Scientometrics: an international journal for all quantitative aspects of the science of science, communication in science and science policy, 2022; 127(11):6557-6577
Statement of Responsibility
Yang Zhang, Rongying Zhao, Yufei Wang, Haihua Chen, Adnan Mahmood, Munazza Zaib, Wei Emma Zhang, Quan Z. Sheng
Conference Name
Abstract
Citations play a fundamental role in supporting authors’ contribution claims throughout a scientific paper. Labelling citation instances with different function labels is indispensable for understanding a scientific text. A single citation is the linkage between two scientific papers in the citation network. These citations encompass rich native information, including context of the citation, citation location, citing and cited paper titles, DOI, and the website’s URL. Nevertheless, previous studies have ignored such rich native information during the process of datasets’ accumulation, thereby resulting in a lack of comprehensive yet significantly valuable features for the citation function classification task. In this paper, we argue that such important information should not be ignored, and accordingly, we extract and integrate all of the native information features into different neural text representation models via trainable embeddings and free text. We first construct a new dataset entitled, NI-Cite, comprising a large number of labelled citations with five key native features (Citation Context, Section Name, Title, DOI, Web URL) against each dataset instance. In addition, we propose to exploit the recently developed text representation models integrated with such information to evaluate the performance of citation function classification task. The experimental results demonstrate that the native information features suggested in this paper enhance the overall classification performance.
School/Discipline
Dissertation Note
Provenance
Description
Published online: 16 January 2022
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© Akadémiai Kiadó, Budapest, Hungary 2022, corrected publication 2022