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| Tuvastamine, mis tekstis ei juhtunud× | Informatsiooni ekstraheerimine× | |
|---|---|---|
| Valdkond | Tekstikaeve | Tekstikaeve |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | 2001 (NegEx); scope learning formalised by 2009 | — |
| Looja≠ | Chapman et al. (NegEx algorithm, 2001); Morante & Daelemans (scope learning, 2009) | — |
| Tüüp≠ | NLP information-extraction task | NLP structured-information task |
| Algallikas≠ | 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 ↗ | Cowie, J. & Lehnert, W. (1996). Information Extraction. Communications of the ACM. DOI ↗ |
| Rööpnimetused | negation scope identification, negation cue detection, Olumsuzlama Tespiti (Negation Detection) | IE, structured information extraction, Bilgi Çıkarma (Information Extraction) |
| Seotud≠ | 6 | 4 |
| Kokkuvõte≠ | 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. | Information extraction (IE) is a natural-language-processing task that converts unstructured text into structured information — such as events, relations, and attributes — so that facts buried in free-form documents become machine-readable records. The task was consolidated in early surveys by Cowie and Lehnert (1996) and later by Grishman (2012). |
| ScholarGateAndmestik ↗ |
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