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Identification of citation and cited texts for fine‐grained citation content analysis
Author(s) -
Ou Shiyan,
Kim Hyonil
Publication year - 2019
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.156
Subject(s) - citation , identification (biology) , ranking (information retrieval) , computer science , citation analysis , information retrieval , similarity (geometry) , scientific literature , data science , library science , artificial intelligence , biology , botany , paleontology , image (mathematics)
Citation content Analysis is very useful to reveal the nature of citations among scientific papers. However, most of previous studies had been done only from the side of citing papers and ignored the side of cited papers. We proposed a similarity‐based unsupervised citation text identification method and a ranking‐based supervised cited text identification method. Based on the identified citation and cited texts, we performed a fine‐grained citation analysis and found there was a difference in the cited texts between the citing papers in different disciplines.