Salīdzināt metodes

	Zinātniskā teksta ieguve	Nosaukuma entītiju atpazīšana (NER)
Nozare	Teksta ieguve	Teksta ieguve
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2019–2020 (modern transformer era); roots in earlier computational linguistics	—
Autors	Community-developed; SciBERT (Beltagy et al., 2019) and SPECTER (Cohan et al., 2020) are landmark models	—
Tips	NLP pipeline for scientific literature	NLP sequence-labelling task
Pirmavots	Beltagy, I., Lo, K., & Cohan, A. (2019). SciBERT: A Pretrained Language Model for Scientific Text. EMNLP 2019.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Citi nosaukumi	Bilimsel Metin Madenciliği, scholarly NLP, academic text mining, scientific literature mining	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Saistītās	4	3
Kopsavilkums	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.	Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use.