手法を比較

	Speculation Detection	固有表現抽出（NER）
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	1996 (lexicon approach); 2010 (CoNLL shared task)	—
提唱者	Hyland, K. (lexicon-based framing, 1996); Farkas et al. (shared-task benchmark, 2010)	—
種類	NLP text-classification task	NLP sequence-labelling task
原典	Hyland, K. (1996). Writing Without Conviction? Hedging in Science Research Articles. Applied Linguistics, 17(4), 433-454.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
別名	hedging detection, epistemic modality analysis, hedge detection, Belirsizlik / Spekülasyon Tespiti (Hedging)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
関連	5	3
概要	Speculation detection, also known as hedging analysis, is a natural-language-processing task that identifies epistemic uncertainty markers — words and phrases such as 'may', 'possibly', 'it is suggested that' — within scientific, biomedical, and news texts. Formalised by Hyland (1996) for scientific writing and benchmarked by the CoNLL-2010 shared task, the method reveals where authors signal incomplete knowledge, tentativeness, or distance from a claim rather than asserting facts directly.	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.