Salīdzināt metodes

	Negācijas noteikšana	Analīze ar atkarību (Dependency Parsing)
Nozare	Teksta ieguve	Teksta ieguve
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2001 (NegEx); scope learning formalised by 2009	—
Autors	Chapman et al. (NegEx algorithm, 2001); Morante & Daelemans (scope learning, 2009)	—
Tips	NLP information-extraction task	NLP syntactic-analysis task
Pirmavots	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.	Nivre, J. (2005). Dependency Grammar and Dependency Parsing. MSI Report.
Citi nosaukumi	negation scope identification, negation cue detection, Olumsuzlama Tespiti (Negation Detection)	syntactic dependency analysis, dependency tree parsing, Bağımlılık Ayrıştırma (Dependency Parsing)
Saistītās	6	3
Kopsavilkums	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.	Dependency parsing is a natural-language-processing task that reveals the syntactic dependency relations between the words of a sentence as a tree structure. Surveyed in the dependency-grammar tradition by Nivre (2005) and made fast and accurate with neural networks by Chen and Manning (2014), it is commonly used as a prerequisite step for information extraction and relation detection.