Порівняння методів

	Розпізнавання іменованих сутностей (NER)	Вилучення відношень	Науковий текстовий майнінг
Галузь	Інтелектуальний аналіз тексту	Інтелектуальний аналіз тексту	Інтелектуальний аналіз тексту
Родина	Process / pipeline	Process / pipeline	Process / pipeline
Рік появи	—	—	2019–2020 (modern transformer era); roots in earlier computational linguistics
Автор методу	—	—	Community-developed; SciBERT (Beltagy et al., 2019) and SPECTER (Cohan et al., 2020) are landmark models
Тип	NLP sequence-labelling task	NLP information-extraction task	NLP pipeline for scientific literature
Основоположне джерело	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.	Zelenko, D., Aone, C. & Richardella, A. (2003). Kernel Methods for Relation Extraction. Journal of Machine Learning Research, 3, 1083-1106.	Beltagy, I., Lo, K., & Cohan, A. (2019). SciBERT: A Pretrained Language Model for Scientific Text. EMNLP 2019.
Інші назви	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)	semantic relation extraction, İlişki Çıkarma (Relation Extraction)	Bilimsel Metin Madenciliği, scholarly NLP, academic text mining, scientific literature mining
Пов'язані	3	4	4
Підсумок	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.	Relation extraction is a natural-language-processing task that detects and classifies the semantic relations that hold between entities mentioned in text. Building on early kernel-based methods (Zelenko and colleagues, 2003) and later neural matching approaches (Baldini Soares and colleagues, 2019), it turns free-form text into structured facts of the form entity–relation–entity.	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.