Порівняння методів

	Науковий текстовий майнінг	Бібліометричний аналіз
Галузь	Інтелектуальний аналіз тексту	Наукометрія
Родина	Process / pipeline	Process / pipeline
Рік появи	2019–2020 (modern transformer era); roots in earlier computational linguistics	1969 (term coined); practice dates to 1920s–1930s
Автор методу	Community-developed; SciBERT (Beltagy et al., 2019) and SPECTER (Cohan et al., 2020) are landmark models	Alan Pritchard (coined term); earlier quantitative work by Paul Otlet (1934) and S. C. Bradford (1934)
Тип	NLP pipeline for scientific literature	Quantitative literature analysis
Основоположне джерело	Beltagy, I., Lo, K., & Cohan, A. (2019). SciBERT: A Pretrained Language Model for Scientific Text. EMNLP 2019.	Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 25(4), 348–349.
Інші назви	Bilimsel Metin Madenciliği, scholarly NLP, academic text mining, scientific literature mining	bibliometrics, bibliometric study, bibliometric mapping, publication analysis
Пов'язані	4	6
Підсумок	Scientific text mining is a natural-language-processing pipeline applied to academic literature. Grounded in domain-specific pretrained models such as SciBERT (Beltagy et al., 2019) and SPECTER (Cohan et al., 2020), it automatically extracts hypotheses, methodologies, findings, and scholarly contributions from full-text papers or abstracts, enabling systematic review automation, research-trend analysis, and science mapping at scale.	Bibliometric analysis applies statistical and mathematical methods to bibliographic records — publications, citations, authors, journals, and keywords — to measure and map the structure, output, and intellectual evolution of a research field. It is widely used to identify influential works, prolific authors, productive journals, collaboration networks, and emerging research themes across any academic discipline.