Bandingkan metode

	Deteksi Negasi	Resolusi Ko-referensi
Bidang	Penambangan Teks	Penambangan Teks
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2001 (NegEx); scope learning formalised by 2009	1978
Pencetus	Chapman et al. (NegEx algorithm, 2001); Morante & Daelemans (scope learning, 2009)	Hobbs (1978); Lee et al. (2017, neural end-to-end)
Tipe	NLP information-extraction task	NLP information-extraction task
Sumber perintis	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.	Lee, K. et al. (2017). End-to-end Neural Coreference Resolution. EMNLP.
Alias	negation scope identification, negation cue detection, Olumsuzlama Tespiti (Negation Detection)	coreference, anaphora resolution, Eşgönderim Çözümleme (Coreference Resolution)
Terkait	6	4
Ringkasan	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.	Coreference resolution is a natural-language-processing task that detects when different expressions in a text refer to the same entity — for example a name, a later pronoun, and a descriptive phrase all pointing at one person. Rooted in early linguistic work by Hobbs (1978) and advanced by the end-to-end neural model of Lee et al. (2017), it improves the quality of information extraction and text understanding.