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| Text Infilling× | Benannte Entitätenerkennung (NER)× | |
|---|---|---|
| Fachgebiet | Text Mining | Text Mining |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1953 (cloze); 2019 (neural span infilling) | — |
| Urheber≠ | Wilson L. Taylor (cloze procedure, 1953); modern span infilling by Zhu et al. (2019) | — |
| Typ≠ | NLP conditional text generation task | NLP sequence-labelling task |
| Wegweisende Quelle≠ | Taylor, W.L. (1953). Cloze Procedure: A New Tool for Measuring Readability. Journalism Quarterly, 30(4), 415-433. link ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ |
| Aliasnamen≠ | cloze procedure, cloze test, masked language modeling, span infilling | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) |
| Verwandt≠ | 4 | 3 |
| Zusammenfassung≠ | 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. | Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use. |
| ScholarGateDatensatz ↗ |
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