Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Detecția Evenimentelor× | Răspunsul la întrebări (QA)× | |
|---|---|---|
| Domeniu | Mineritul textelor | Mineritul textelor |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției | — | — |
| Autorul original | — | — |
| Tip≠ | NLP information-extraction task | NLP text-comprehension task |
| Sursa seminală≠ | Doddington, G. et al. (2004). The Automatic Content Extraction (ACE) Program — Tasks, Data, and Evaluation. LREC. link ↗ | Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP. DOI ↗ |
| Denumiri alternative≠ | event extraction, Olay Tespiti (Event Detection) | QA, machine reading comprehension, Soru Cevaplama (Question Answering) |
| Înrudite | 4 | 4 |
| Rezumat≠ | Event detection is a natural-language-processing information-extraction task that finds events, historical developments, and action expressions in text and classifies them by type. It grew out of the Automatic Content Extraction (ACE) program described by Doddington et al. (2004) and is widely used in news analysis and historical research. | Question answering is a natural-language-processing task that automatically answers natural-language questions grounded in a given context passage, using either extractive or generative approaches. The task was crystallised by the SQuAD benchmark of Rajpurkar et al. (2016), and later models such as XLNet (Yang et al., 2019) pushed reading-comprehension accuracy higher. |
| ScholarGateSet de date ↗ |
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