Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Извлечение аргументации× | Обнаружение субъективности× | |
|---|---|---|
| Область | Интеллектуальный анализ текста | Интеллектуальный анализ текста |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2016 | — |
| Автор метода≠ | Lippi & Torroni (state-of-the-art survey) | — |
| Тип≠ | NLP information-extraction task | NLP text-classification task |
| Основополагающий источник≠ | Lippi, M. & Torroni, P. (2016). Argumentation Mining: State of the Art and Emerging Trends. ACM Transactions on Internet Technology, 16(2), Article 10, 1-25. DOI ↗ | Wiebe, J., Wilson, T. & Cardie, C. (2005). Annotating Expressions of Opinions and Emotions in Language. Language Resources and Evaluation, 39(2-3), 165-210. DOI ↗ |
| Другие названия | argumentation mining, argument extraction, Argüman Madenciliği | subjective vs objective classification, subjectivity classification, Öznellik Tespiti (Subjectivity Detection) |
| Связанные≠ | 4 | 3 |
| Сводка≠ | Argument mining is a natural-language-processing task that automatically detects claims, premises and the argumentative structures that link them within text. Consolidated as a field by Lippi and Torroni's 2016 state-of-the-art survey, it is applied to scientific writing, legal documents and debate analysis to turn free-form argumentation into structured, analysable units. | Subjectivity detection is a natural-language-processing task that classifies whether a sentence or document conveys objective (neutral information) or subjective (personal opinion, emotion) content. Grounded in the opinion-annotation work of Wiebe and colleagues (2005) and Pang and Lee (2004), it is most often used as a preliminary step before sentiment analysis. |
| ScholarGateНабор данных ↗ |
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