方法对比
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| 语篇分析× | 文本分类× | |
|---|---|---|
| 领域 | 文本挖掘 | 文本挖掘 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1988 (RST); 2008 (PDTB 2.0) | — |
| 提出者≠ | Mann & Thompson (RST); Prasad et al. (PDTB) | — |
| 类型≠ | NLP discourse-structure analysis task | Supervised NLP classification task |
| 开创性文献≠ | Mann, W. C. & Thompson, S. A. (1988). Rhetorical Structure Theory: Toward a functional theory of text organization. Text, 8(3), 243-281. DOI ↗ | Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer. DOI ↗ |
| 别名 | rhetorical structure analysis, RST parsing, PDTB parsing, Söylem Ayrıştırma (Discourse Parsing) | text categorization, document classification, topic classification, metin sınıflandırma |
| 相关≠ | 3 | 4 |
| 摘要≠ | Discourse parsing is a natural-language-processing task that models the rhetorical relations between sentences and paragraphs of a text — relations such as cause, contrast, and elaboration — and represents them as a tree structure. It works within established frameworks, principally Rhetorical Structure Theory (RST), introduced by Mann and Thompson in 1988, and the Penn Discourse TreeBank (PDTB), released by Prasad and colleagues in 2008. | Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples. |
| ScholarGate数据集 ↗ |
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