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Kunnath, Suchetha N.; Herrmannova, Drahomira; Pride, David and Knoth, Petr
(2022).
DOI: https://doi.org/10.1162/qss_a_00159
Abstract
The aim of this literature review is to examine the current state-of-the-art in the area of citation classification. In particular, we investigate the approaches for characterising citations based on their semantic type. We conduct this literature review as a metaanalysis covering 60 scholarly articles in this domain. Although we included some of the manual pioneering works in this review, more emphasis is given on the later automated methods, which use Machine Learning and Natural Language Processing (NLP) for analysing the fine-grained linguistic features in the surrounding text of citations. The sections are organised based on the steps involved in the pipeline for citation classification. Specifically, we explore the existing classification schemes, datasets, pre-processing methods, the extraction of contextual and non-contextual features and the different types of classifiers and evaluation approaches. The review highlights the importance of identifying the citation types for research evaluation, the challenges faced by the researchers in the process and the existing research gaps in this field.
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- Item ORO ID
- 79616
- Item Type
- Journal Item
- Project Funding Details
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Funded Project Name Project ID Funding Body OU Scientometrics PhD Studentship 4133 Jisc AI Chemist under the cooperation of IRIS.ai with The Open University 309594 NRC UT-Battelle DE-AC05-00OR22725 UT-Battelle - Keywords
- Citation Classification; Citation Type; Citation Function; Citation Polarity; Citation Importance; Citation Context
- Academic Unit or School
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Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Research Group
- Big Scientific Data and Text Analytics Group (BSDTAG)
- Copyright Holders
- © 2021 Sutchetha N. Kunnath et al.
- Depositing User
- Suchetha Nambanoor Kunnath