Year: 2021
Type: review
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 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, preprocessing methods, extraction of contextual and noncontextual 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.
Source: Quantitative Science Studies
DOI: 10.1162/qss_a_00159
Institutions: The Open University, Oak Ridge National Laboratory
Language: English
Subfield: Library and Information Sciences
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $800
Award: G893080921
OpenAlex ID: W3207782692