方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 论证挖掘× | 主观性检测× | |
|---|---|---|
| 领域 | 文本挖掘 | 文本挖掘 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2016 | — |
| 提出者≠ | Lippi & Torroni (state-of-the-art survey) | — |
| 类型≠ | NLP information-extraction task | NLP text-classification task |
| 开创性文献≠ | 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 ↗ |
| 别名 | argumentation mining, argument extraction, Argüman Madenciliği | subjective vs objective classification, subjectivity classification, Öznellik Tespiti (Subjectivity Detection) |
| 相关≠ | 4 | 3 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|