A meta-analysis of semantic classification of citations

Journal Article · 04 February 2022 · Quantitative science studies

DOI: https://doi.org/10.1162/qss_a_00159 · OSTI ID:1831680

Kunnath, Suchetha N.  ^[1]; Herrmannova, Drahomira  ^[2]; Pride, David  ^[1]; Knoth, Petr  ^[1]

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1. Open University, Milton Keynes (United Kingdom)
2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

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 characterizing citations based on their semantic type. We conduct this literature review as a meta-analysis covering 60 scholarly articles in this domain. Although we included some of the manual pioneering works in this review, more emphasis is placed on the later automated methods, which use Machine Learning and Natural Language Processing (NLP) for analyzing the fine-grained linguistic features in the surrounding text of citations. The sections are organized based on the steps involved in the pipeline for citation classification. Specifically, we explore the existing classification schemes, data sets, pre-processing methods, 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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: Jisc; USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1831680
• Journal Information: Quantitative science studies, Journal Name: Quantitative science studies Journal Issue: 4 Vol. 2; ISSN 2641-3337
• Publisher: MIT PressCopyright Statement
• Country of Publication: United States
• Language: English