Lexical overlap between author keywords and titles as a measurable characteristic of scholarly metadata: A corpus-based analysis
https://doi.org/10.24069/sep.26.11.50
Abstract
Author keywords are used in the search, indexing and thematic classification of scholarly publications, yet existing studies treat their overlap with the title as an aggregate measure and do not offer a reproducible calculation procedure at the level of individual manuscripts and the corpus. This article examines token-level overlap between author keywords and titles of scholarly manuscripts. The purpose of the study is to develop and apply a reproducible procedure for calculating this overlap at the level of individual manuscripts and the corpus as a whole. The material consisted of 779 English-language manuscripts submitted to an international scholarly journal in 2022–2026. The analysis used manuscript titles and author keywords. Preprocessing included text normalization, tokenization, identification of overlapping and new tokens, calculation of TKOI (Title Keyword Overlap Index), the complementary TKEI (Title Keyword Expansion Index), and absolute token counts. The calculations were performed in Python using the pandas, NLTK, and matplotlib libraries. In the main preprocessing mode, 43.4% of keyword tokens overlapped with title tokens, while 56.6% were absent from titles. With the two alternative preprocessing modes, the share of overlapping tokens ranged from 42.9% to 46.1%. Across all three variants, new tokens outnumbered overlapping tokens. In 34 of 779 manuscripts, surface token overlap with the title was completely absent. The median TKOI was 0.444444, and the interquartile range was 0.314286. The proposed procedure can be used in corpus-based analysis of scholarly metadata to examine the relationship between author keywords and manuscript titles.
Keywords
scholarly metadata,
author keywords,
lexical overlap,
corpus analysis,
scholarly publications,
token-level overlap
Supporting agencies: This paper has been supported by RUDN University project No. 061012-0-000 ‘Linguistic-cognitive forecasting tools in corporate discourse’
About the Authors
Elena N. Malyuga
RUDN University
Russian Federation
Russian Federation
Dr. Sci. (Linguistics), Professor, Head of Foreign Languages Department, Faculty of Economics, RUDN University; Editor-in-Chief of Training, Language and Culture research journal; author of monographs, textbooks, teaching aids, research articles on functional pragmatics, sociolinguistics, professional and business communication, corporate communication, and corpus linguistics.
Elizaveta G. Grishechko
RUDN University
Russian Federation
Russian Federation
Cand. Sci. (Linguistics), Senior Lecturer in the Foreign Languages Department, Faculty of Economics, RUDN University; Executive Secretary of Training, Language and Culture research journal; research interests include issues of corpus linguistics, computational linguistics, digital linguistics, and natural language processing.
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For citations:
Malyuga E.N., Grishechko E.G. Lexical overlap between author keywords and titles as a measurable characteristic of scholarly metadata: A corpus-based analysis. Science Editor and Publisher. (In Russ.) https://doi.org/10.24069/sep.26.11.50
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