Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Respuesta a preguntas (QA)× | Reconocimiento de entidades nombradas (NER)× | |
|---|---|---|
| Campo | Minería de texto | Minería de texto |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen | — | — |
| Autor original | — | — |
| Tipo≠ | NLP text-comprehension task | NLP sequence-labelling task |
| Fuente seminal≠ | Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP. DOI ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ |
| Alias | QA, machine reading comprehension, Soru Cevaplama (Question Answering) | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) |
| Relacionados≠ | 4 | 3 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
|
|