Uporedite metode

	Detekcija negacije	Klasifikacija teksta
Oblast	Rudarenje teksta	Rudarenje teksta
Porodica	Process / pipeline	Process / pipeline
Godina nastanka	2001 (NegEx); scope learning formalised by 2009	—
Tvorac	Chapman et al. (NegEx algorithm, 2001); Morante & Daelemans (scope learning, 2009)	—
Tip	NLP information-extraction task	Supervised NLP classification task
Temeljni izvor	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.
Drugi nazivi	negation scope identification, negation cue detection, Olumsuzlama Tespiti (Negation Detection)	text categorization, document classification, topic classification, metin sınıflandırma
Srodne	6	4
Sažetak	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.