Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Rozpoznávání pojmenovaných entit (NER)× | Odpovídání na otázky (QA)× | |
|---|---|---|
| Obor | Dolování textu | Dolování textu |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku | — | — |
| Tvůrce | — | — |
| Typ≠ | NLP sequence-labelling task | NLP text-comprehension task |
| Původní zdroj≠ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ | Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP. DOI ↗ |
| Další názvy | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) | QA, machine reading comprehension, Soru Cevaplama (Question Answering) |
| Příbuzné≠ | 3 | 4 |
| Shrnutí≠ | 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. | 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. |
| ScholarGateDatová sada ↗ |
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