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| Uzupełnianie tekstu× | Klasyfikacja Tekstu× | |
|---|---|---|
| Dziedzina | Eksploracja tekstu | Eksploracja tekstu |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1953 (cloze); 2019 (neural span infilling) | — |
| Twórca≠ | Wilson L. Taylor (cloze procedure, 1953); modern span infilling by Zhu et al. (2019) | — |
| Typ≠ | NLP conditional text generation task | Supervised NLP classification task |
| Źródło pierwotne≠ | Taylor, W.L. (1953). Cloze Procedure: A New Tool for Measuring Readability. Journalism Quarterly, 30(4), 415-433. link ↗ | 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 ↗ |
| Inne nazwy≠ | cloze procedure, cloze test, masked language modeling, span infilling | text categorization, document classification, topic classification, metin sınıflandırma |
| Pokrewne | 4 | 4 |
| Podsumowanie≠ | Text infilling is a natural-language-processing task that completes missing words, phrases, or spans in a document by exploiting the surrounding context. Introduced as the cloze procedure by Wilson L. Taylor in 1953 as a readability measure, it was reformulated for neural models by Zhu et al. (2019) and is now used for data augmentation, writing assistance, and language-model evaluation. | 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. |
| ScholarGateZbiór danych ↗ |
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