Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Системи запитань-відповідей (QA)× | Сентимент-аналіз× | |
|---|---|---|
| Галузь | Інтелектуальний аналіз тексту | Інтелектуальний аналіз тексту |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи | — | — |
| Автор методу | — | — |
| Тип≠ | NLP text-comprehension task | NLP text-classification task |
| Основоположне джерело≠ | Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP. DOI ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Інші назви | QA, machine reading comprehension, Soru Cevaplama (Question Answering) | opinion mining, polarity detection, duygu analizi |
| Пов'язані≠ | 4 | 3 |
| Підсумок≠ | 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. | Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models. |
| ScholarGateНабір даних ↗ |
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