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| Анализ на рамки× | Анализ на настроенията× | Класификация на текст× | |
|---|---|---|---|
| Област | Извличане на текст | Извличане на текст | Извличане на текст |
| Семейство | Process / pipeline | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1982 | — | — |
| Създател≠ | Charles J. Fillmore | — | — |
| Тип≠ | NLP frame-semantic parsing task | NLP text-classification task | Supervised NLP classification task |
| Основополагащ източник≠ | Fillmore, C. J. (1982). Frame Semantics. In Linguistics in the Morning Calm. Seoul: Hanshin Publishing. ISBN: 9788970050355 | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. 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 ↗ |
| Други названия≠ | frame semantics, frame-semantic parsing, FrameNet analysis, Çerçeve Analizi (Frame Analysis) — NLP | opinion mining, polarity detection, duygu analizi | text categorization, document classification, topic classification, metin sınıflandırma |
| Свързани≠ | 4 | 3 | 4 |
| Резюме≠ | Frame analysis is a FrameNet-based natural-language-processing task that detects the semantic frames evoked in text and the participant roles (frame-evoking elements and frame elements, FE) that fill them. Rooted in Charles Fillmore's frame semantics (1982) and operationalised by the Berkeley FrameNet Project (Baker et al., 1998), it is widely used to analyse media discourse and political text. | 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. | 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. |
| ScholarGateНабор от данни ↗ |
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