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| Deteksi Negasi× | Pengenalan Entitas Bernama (NER)× | |
|---|---|---|
| Bidang | Penambangan Teks | Penambangan Teks |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 2001 (NegEx); scope learning formalised by 2009 | — |
| Pencetus≠ | Chapman et al. (NegEx algorithm, 2001); Morante & Daelemans (scope learning, 2009) | — |
| Tipe≠ | NLP information-extraction task | NLP sequence-labelling 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. DOI ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ |
| Alias | negation scope identification, negation cue detection, Olumsuzlama Tespiti (Negation Detection) | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) |
| Terkait≠ | 6 | 3 |
| 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. | Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use. |
| ScholarGateSet data ↗ |
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