Comparar métodos

	Detección de negaciones	Clasificación de Texto
Campo	Minería de texto	Minería de texto
Familia	Process / pipeline	Process / pipeline
Año de origen	2001 (NegEx); scope learning formalised by 2009	—
Autor original	Chapman et al. (NegEx algorithm, 2001); Morante & Daelemans (scope learning, 2009)	—
Tipo	NLP information-extraction task	Supervised NLP classification task
Fuente seminal	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.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Alias	negation scope identification, negation cue detection, Olumsuzlama Tespiti (Negation Detection)	text categorization, document classification, topic classification, metin sınıflandırma
Relacionados	6	4
Resumen	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.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.