Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Выявление событий× | Обработка естественного языка (NLP)× | |
|---|---|---|
| Область | Интеллектуальный анализ текста | Интеллектуальный анализ текста |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления | — | — |
| Автор метода | — | — |
| Тип≠ | NLP information-extraction task | NLP text-comprehension task |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия≠ | event extraction, Olay Tespiti (Event Detection) | QA, machine reading comprehension, Soru Cevaplama (Question Answering) |
| Связанные | 4 | 4 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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