방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 담화 분석× | 논증 채굴× | |
|---|---|---|
| 분야 | 텍스트 마이닝 | 텍스트 마이닝 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1988 (RST); 2008 (PDTB 2.0) | 2016 |
| 창시자≠ | Mann & Thompson (RST); Prasad et al. (PDTB) | Lippi & Torroni (state-of-the-art survey) |
| 유형≠ | NLP discourse-structure analysis task | NLP information-extraction task |
| 원전≠ | Mann, W. C. & Thompson, S. A. (1988). Rhetorical Structure Theory: Toward a functional theory of text organization. Text, 8(3), 243-281. DOI ↗ | 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 ↗ |
| 별칭≠ | rhetorical structure analysis, RST parsing, PDTB parsing, Söylem Ayrıştırma (Discourse Parsing) | argumentation mining, argument extraction, Argüman Madenciliği |
| 관련≠ | 3 | 4 |
| 요약≠ | 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. | 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. |
| ScholarGate데이터셋 ↗ |
|
|