Сравнение методов

	Обнаружение отрицания	Зависимостный парсинг
Область	Интеллектуальный анализ текста	Интеллектуальный анализ текста
Семейство	Process / pipeline	Process / pipeline
Год появления	2001 (NegEx); scope learning formalised by 2009	—
Автор метода	Chapman et al. (NegEx algorithm, 2001); Morante & Daelemans (scope learning, 2009)	—
Тип	NLP information-extraction task	NLP syntactic-analysis task
Основополагающий источник	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.
Другие названия	negation scope identification, negation cue detection, Olumsuzlama Tespiti (Negation Detection)	syntactic dependency analysis, dependency tree parsing, Bağımlılık Ayrıştırma (Dependency Parsing)
Связанные	6	3
Сводка	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.