เปรียบเทียบวิธี

	การตรวจจับนิเสธ	การรู้จำหน่วยคำนาม (Named Entity Recognition - NER)
สาขาวิชา	การทำเหมืองข้อความ	การทำเหมืองข้อความ
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2001 (NegEx); scope learning formalised by 2009	—
ผู้ริเริ่ม	Chapman et al. (NegEx algorithm, 2001); Morante & Daelemans (scope learning, 2009)	—
ประเภท	NLP information-extraction task	NLP sequence-labelling task
แหล่งต้นตำรับ	Chapman, W.W., Bridewell, W., Hanbury, P., Cooper, G.F., & Buchanan, B.G. (2001). A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries. Journal of the American Medical Informatics Association, 8(6), 606-614.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
ชื่อเรียกอื่น	negation scope identification, negation cue detection, Olumsuzlama Tespiti (Negation Detection)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
ที่เกี่ยวข้อง	6	3
สรุป	Negation detection is a natural-language-processing task that locates negation cues in text — words or phrases such as 'no', 'not', 'without', or 'denies' — and determines the span of text (the scope) whose meaning those cues invert. Formalised for clinical text by Chapman et al. (2001) with the NegEx algorithm and extended to scope learning in biomedical literature by Morante and Daelemans (2009), the method is essential wherever the difference between a finding being present and its being explicitly ruled out carries real consequences.	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.