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| Điền văn bản× | Nhận dạng thực thể có tên (NER)× | Phân tích Cảm xúc× | |
|---|---|---|---|
| Lĩnh vực | Khai phá văn bản | Khai phá văn bản | Khai phá văn bản |
| Họ | Process / pipeline | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 1953 (cloze); 2019 (neural span infilling) | — | — |
| Người khởi xướng≠ | Wilson L. Taylor (cloze procedure, 1953); modern span infilling by Zhu et al. (2019) | — | — |
| Loại≠ | NLP conditional text generation task | NLP sequence-labelling task | NLP text-classification task |
| Công trình gốc≠ | 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 ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Tên gọi khác≠ | cloze procedure, cloze test, masked language modeling, span infilling | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) | opinion mining, polarity detection, duygu analizi |
| Liên quan≠ | 4 | 3 | 3 |
| Tóm tắt≠ | 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. | 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. |
| ScholarGateBộ dữ liệu ↗ |
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