Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Обработка естественного языка (NLP)× | Распознавание именованных сущностей (NER)× | |
|---|---|---|
| Область | Интеллектуальный анализ текста | Интеллектуальный анализ текста |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления | — | — |
| Автор метода | — | — |
| Тип≠ | NLP text-comprehension task | NLP sequence-labelling task |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | QA, machine reading comprehension, Soru Cevaplama (Question Answering) | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) |
| Связанные≠ | 4 | 3 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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