手法を比較

	固有表現抽出（NER）	科学テキストマイニング
分野	テキストマイニング	テキストマイニング
系統	Process / pipeline	Process / pipeline
提唱年	—	2019–2020 (modern transformer era); roots in earlier computational linguistics
提唱者	—	Community-developed; SciBERT (Beltagy et al., 2019) and SPECTER (Cohan et al., 2020) are landmark models
種類	NLP sequence-labelling task	NLP pipeline for scientific literature
原典	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.	Beltagy, I., Lo, K., & Cohan, A. (2019). SciBERT: A Pretrained Language Model for Scientific Text. EMNLP 2019.
別名	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)	Bilimsel Metin Madenciliği, scholarly NLP, academic text mining, scientific literature mining
関連	3	4
概要	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.	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.