Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Разрешение кореференции× | Анализ тональности× | |
|---|---|---|
| Область | Интеллектуальный анализ текста | Интеллектуальный анализ текста |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1978 | — |
| Автор метода≠ | Hobbs (1978); Lee et al. (2017, neural end-to-end) | — |
| Тип≠ | NLP information-extraction task | NLP text-classification task |
| Основополагающий источник≠ | Lee, K. et al. (2017). End-to-end Neural Coreference Resolution. EMNLP. link ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Другие названия | coreference, anaphora resolution, Eşgönderim Çözümleme (Coreference Resolution) | opinion mining, polarity detection, duygu analizi |
| Связанные≠ | 4 | 3 |
| Сводка≠ | Coreference resolution is a natural-language-processing task that detects when different expressions in a text refer to the same entity — for example a name, a later pronoun, and a descriptive phrase all pointing at one person. Rooted in early linguistic work by Hobbs (1978) and advanced by the end-to-end neural model of Lee et al. (2017), it improves the quality of information extraction and text understanding. | 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. |
| ScholarGateНабор данных ↗ |
|
|